Abstract: To detail the schedules for crude oil operations, it is necessary to optimize multiple objectives. A dual population algorithm is proposed,being called bare bone particle swarm optimization and NSGA-II algorithm, BPGA, which is based on the improved bare bone particle swarm optimization algorithm (I-BBPSO) and non-dominated sorting genetic algorithm II (NSGA-II). It controls population exchange through Pareto difference entropy, which optimizes all the costs of the charging tanks, switching the charging tanks, mixing crude oil in the pipeline and mixing crude oil in the bottom of the tanks. The proposed algorithm is applied to an industrial example and compared with several representative evolutionary multi-objective algorithms and shows its feasibility and validity .