In electrical engineering, designing a heterogeneous system must be processed by means of a global approach, also called “systemic approach”. Indeed, the existence of interactions between the different sub-systems and the multi-field nature the device strongly influence the system performances. The optimisation of the whole system with regard to several objectives has to be achieved by including all these couplings. The work done in this thesis deals with multiobjective evolutionary optimisation applied to the optimal design. Optimisation issues resulting of this process are complex. For that purpose, multiobjective Pareto evolutionary algorithms have been chosen. To select genetic operators, we have developed a self-adaptation which increases the exploration robustness. Through the design of several electrical engineering systems (example of an electrical vehicle,…), global design oriented models have been proposed from which algorithms are used to seek the best trade-offs versus optimisation criteria (losses, mass,…). Exploiting optimisation results shows that this process constitutes a very efficient design tool if performance and coupling analysis issues are concerned.