To optimize the grate cooler control parameters and improve the heat exchange efficiency, the modified entropy production numbers caused by heat transfer and viscous dissipation are respectively taken as objective functions, and the genetic algorithm is used for grate cooler parameters multi-objective optimization. In order to increase the population diversity of the multi-objective genetic algorithms and improve the local search ability of the algorithms, some improvements are made to non-dominated sorting genetic algorithm (NSGA-II). The operation modes of multi-group and multi-crossing operators are constructed. According to the contribution of sub-populations to the optimal solution set the size of the sub-populations size is adaptively adjusted. The local search algorithm is used to improve the local search ability of the algorithm. The effectiveness of the proposed algorithm is verified by some benchmark multi-objective optimization problems. According to the optimal solution set, the optimal control scheme for the minimum cooling fan power is given. The comparison with the actual data of the production line verifies the optimization effect.