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In this paper, we propose a multi-objective genetic algorithm for effectively solving multistage-based job processing schedules in FMS environment. The proposed method is random-weight approach to obtaining a variable search direction toward Pareto solution. The objectives are to minimize the makespan and the total flow time, simultaneously. The feasibility and adaptability of the proposed moGA are investigated through experimental results.