Abstract: In this paper, we propose an improved discrete artificial bee colony algorithm based on the Pareto solution to solve the problem of a flexible job shop with multiple target constraints. Since the selection probability for classical artificial colonies is not applicable to multi-objective problems, we redefine the selection probability to depend on ranking. We also use the neighborhood search method based on a mutation operation for the local search, and apply a hybrid-column crossover operator to improve population diversity. Then, we attach the Pareto solution set to the harmonic average distance and update the Pareto solution set. We verified the effectiveness of the proposed algorithm in solving the multi-objective flexible job-shop scheduling problem in a case test and simulation experiment.