The aim of this project is to integrate uncertainty analysis in a thermo-economic optimization framework to be used as decision making support in the design of energy systems.Tree comprehensive thermo-economic models of fuel cells systems have been developed: a PEMFC, a SOFC and a hybrid SOFC-gas turbine concept to serve as a validation basis for the development. The focuss has been put on the adaptation of the multi-objective optimisation strategy. A Monte-Carlo based approach has been developed to generate the validity range of each point of the Pareto curve resulting from a conventional thermo-economic optimization in the uncertain parameter domain. The use of stochastic programming by incorporating randomly selected uncertain parameters in the multi-objective optimization strategy using evolutionary algorithm has been studied. The comparison with a conventional approach reveals that different decision will be taken when considering uncertain parameters. The development of a multi-period formulation reveals to be necessary in order to account for the flexibility aspects that allow optimal set points to compensate the uncertain performance of equipment. Further work are still required to improve the method by incorporating stochastic aspects in the optimization procedure and by developing efficient methods to solve the multi-period problem in the multi-objective process design optimization framework.