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This work presents a synthesis method that leads to the preliminary design of industrial energy systems. Such systems are composed of several technologies that transform, through a set of physical unit operations, raw materials and energy into products and energy services. The purpose of the preliminary synthesis is to define which technologies form the system, to calculate their technical characteristics, their operating conditions and their interactions, based on performance indicators such as for example the cost of the system or its environmental impact. This area is studied since a long time and many synthesis approaches are available. However, these methods have significant limitations, particularly in their ability to face the study of more and more complex systems. The main goal of the method presented in this work is to enable the study of large systems and promote the use of the experience gained from previous studies in the form of models and methodological approaches. The synthesis method of industrial energy systems is based on the overall system optimization using a model that is obtained by assembling several modules, representing each a set of physical unit operations and whose equations are formulated with the black box technique, and an integration model that can represent the possible interactions within the system. Optimization is performed using a multi-objective evolutionary algorithm, whose objective functions are defined on the basis of calculation of several performance indicators based on the model of the system. To simplify the resolution of this complex system, the optimization problem is decomposed into a master problem, responsible for calculating the characteristics of the units and their operating conditions, and an optimization slave subproblem, which selects the units being part of the system and their interconnections. To ensure its robustness, the slave subproblem is formulated as a mixed-integer linear optimization problem. The slave subproblem is formulated by using process integration techniques, which are extended in this work to allow the synthesis of multiple heat and mass transfer networks. The synthesis problem can thus be defined using an explicit superstructure or by one generated automatically, either implicit or explicit. This work also introduces a technique that greatly reduces the number of degrees of freedom of the integration model. Instead of separately optimizing each ΔTmin/2 associated with heat streams, a formula is applied to calculate their value from a reference case. The ΔTmin/2 optimization is thus reduced to the optimization of a single decision variable related to the reference case, regardless of the size of the problem. The proposed method uses a set of heterogeneous elements, including flowsheeting software for modeling physical unit operations, mathematical programming tools for the formulation of the integration problem, methods for calculating performance indicators and calculation tools such as the optimization algorithm. This work introduces new tools developed to systematically apply the proposed methodology and to automate recurring operations of data transfer and the call of the various used software. In particular, a syntax description is defined as an abstraction layer to describe and to structure the exchanged information. A computing platform has been created to support the application of the method and to ensure the data transfer between its components. Two case studies are presented to illustrate the various aspects of the synthesis method. A first case, involving the synthesis of two combined cycles, has been chosen to illustrate the different application stages of the method and to show the potential reuse of certain modules. Through the integration techniques, it has been possible to identify potential heat recovery that can increase the performance of one of the cycles beyond what had been expected by experts using conventional simulation techniques. The second case study is about the treatment of waste generated by an industrial site active in the field of fine chemicals. Waste treatment can recover different materials and energy services useful for process units, thereby reducing the quantities purchased in the market. The model of multi-network integration can easily solve the complexity of the problem of waste management in developing strategies for allocating waste to the various treatments available for different objective functions related to operating costs and environmental impact.