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To solve flexible job-shop multiobjective scheduling problem, the optimization model was set up. Considering of the makespan, manufacturing cost and earliness/tardiness penalties, a crowding distance sorting based on multiobjective particle swarm optimization algorithm was proposed. With the elitism strategy, dominant individuals were preserved in evolution process. The shrink of the external population and update of the global best were achieved by the individuals’ crowding distance sorting in descending order. A small ratio mutation was introduced to enhance the diversity of solutions and the global searching capacity of the algorithm. Finally, the feasibility and validity of the method was proved by the simulation results of a flexible job-shop multiobjective scheduling in a workshop.