The ultimate objective of any control strategy is to maximize productivity, and improve the quantity of products and reduce costs. The performance of a bioprocess operating in fed batch production of protein can be obtained in two steps. First, we determine the optimal trajectories (profiles) for the variables of interests and then a genetic algorithm based on a fuzzy logic controller is applied to regulate these variables around these profiles. An optimal feeding profile of a fed batch process based on an evolutionary algorithm is designed. This algorithm is well suited to derive multi-objective optimization, since it involves a set of non-dominated solutions distributed along the Pareto front. Several evolutionary multi-objective optimization algorithms have been developed in which the Non-dominated Sorting Genetic Algorithm NSGA-II is recognized to be very effective to overcome a variety of problems; an optimal control problem, usually solved by several methods considering single-objective dynamic optimization, is worked out.