In the middle or late evolutionary stage of large-scale multi-objective optimization problems(LSMOPs), the differential evolution(DE) algorithm has problems such as diversity shortage and slow convergence. A large-scale multi-objective optimization based on multi-population and multi-strategy differential evolution(LMOMMDE) is proposed. According to the characteristics of individuals in the population, the population is divided into three subpopulations with different levels, and the advantages of the multi-population strategy are used to maintain the diversity of the population. To reduce the probability that the population will fall into local optimum, multiple mutation strategies are introduced for subpopulations on different levels, this operation better balances the diversity and convergence of individual in subpopulations. To ensure the effective exchange of information among different subpopulations, this paper determines the timing of regrouping according to the evolutionary status of the three subpopulations, the individual can fully evolve within the population, and the individual can effectively exchange information under certain conditions. To use more information to generate excellent offspring, the updated subpopulations and their parent subpopulations are combined and generate the next generation subpopulations. To verify the effectiveness of the LMOMMDE, the performance of this algorithm is evaluated on a set of large-scale benchmark problems. The experimental results show that LMOMMDE is significantly better than the comparison algorithms in the two commonly used test indicators IGD and HV.