Multi-objective differential evolution (MODE) algorithm has high computational complexity of selection strategy in solving complex multi-objective optimization problems. To address this issue, a multi-objective differential evolution with data-driven selection strategy (MODE-DDSS) is proposed in this paper. First, the ranking evaluation criteria of optimization solutions is designed, and the ranking evaluation database of optimization solutions based on evaluation criteria is established. Second, a data-driven selection strategy, based on a two-way search mechanism and a non-repeated comparison mechanism, is designed to search and compare the optimal solutions efficiently, and select the optimal solutions. Finally, a multi-objective differential evolution algorithm with data-driven selection strategy is constructed, which reduces the complexity of optimal solution selection operation and improves the optimization efficiency of the algorithm. Experimental results show that the proposed MODE-DDSS algorithm can effectively reduce the number of comparison operations in the selection strategy, and improve the efficiency of the multi-objective differential evolution algorithm in solving complex multi-objective optimization problems.