A multi-objective evolutionary algorithm developed at the Laboratory of Industrial Energy Systems is used to determine the best technological solutions to satisfy the electrical needs of a remote community. Simulations of the community are based on a superstructure in which organic Rankine cycle, diesel engine, photovoltaic and other technology options (heat storage, cogeneration engine, cooling tower, parabolic through,...) are included in the form of modules. These modules model the most significant factors: thermodynamic behaviour, economic trends, gaseous and noise emissions. An objective function including noise level, impact, and location is used to characterise the noise disturbance. Optimising such a superstructure along Pareto curves (optimal trade-off curve between the economic and noise objectives) while simultaneously accounting for time availability is performed by the evolutionary algorithm. Trends on the optimum power technologies as well as the relation to their geographical location are shown for different situations.