Abstract : The work presented in this thesis concerns the development of an optimization methodology for control laws of wastewater treatment plants. This work is based on the use of WWTP process models in order to simulate their operation. These simulations are used by a multi-objective genetic algorithm, NSGA-II. This optimization algorithm allows the search of optimal solutions when multiple objectives are considered (e.g. effluent quality and energy consumption). It also visualizes compromises between various control laws as well as their respective best domains of application. In the first part of this work, the optimization methodology is developed around four main points: the conception of a robust simulation procedure, the choice of input datasets for the simulations, the choice of objectives and constraints to consider and the evaluation of long-term performance and robustness of control laws. This methodology is then applied to the literature case study of BSM1. In the second part of the work, the methodology is applied to the real case study of the Cambrai wastewater treatment plant. This application includes the development of new aspects, such as generation of dynamic input datasets out of daily monitoring measurements of the wastewater treatment plant, as well as simulation of control laws based on oxydo-reduction potential measurements. This application allowed analysis of the compromises between the control law currently tested on the wastewater treatment plant and a new control law foreseen. The benefits of this modification could thus be clearly observed.