A genetic algorithm (GA) for the class of multi-objective optimization problems that appears in the design of robust controllers is presented in this paper. The design of a robust controller is a trade-off problem among competitive objectives such as disturbance rejection, reference tracking, stability against un-modeled dynamics, moderate control effort and so on. However, general methodologies for solving this class of design problems are not easily encountered in the literature because of the complexity of the resultant multi-objective problems. In this paper, popular adaptations of the simple GA, non-dominated sorting genetic algorithms, are used to solve robust control design problems. The structure and operators of this algorithm have been specifically developed for control design problems. The performance of the algorithm is evaluated by solving several test cases and is also compared to the standard algorithms used for the multi-objective design of robust controllers.