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The article presents the idea of a hybrid system for multiobjective optimization. The system consists of the genetic algorithm and the fuzzy logic driver. The genetic algorithm realizes the process of multiobjective optimization. The fuzzy logic driver uses data aggregated by the genetic algorithm and controls the process of evolution by modifying the probability of selection of individuals to the parental pool. The controlling of the evolution process makes it possible to choose the preferred area with pareto-optimal solution. In experiments we investigated the ability of the proposed system to search solutions in a given area of the search space. We compared the results of the standard genetic algorithm and the proposed system. The experiments showed that the proposed system is able to control the process of evolution toward pareto-optimal solutions in the given area of searching.