Most industrial processes are modeled as linear time invariant systems with parametric uncertainties. The design of a robust controller for these plants is formulated as a multiobjective min-max problem where certain performance objectives are minimized with respect to the controller and maximized with respect to the uncertainties. The solution of such a problem is extremely difficult. An approximated two-step approach is proposed in this paper. In the first step, an auxiliary multiobjective minimization problem is solved. The solution to this problem is the set of Pareto optimal controllers. In the second step, these controllers are checked for the worst case of parametric uncertainty by solving a multiobjective maximization problem. The MRCD genetic algorithm is used to solve both multiobjective optimization problems.