Aiming at the disadvantages of slow convergence and easily falling into local optimum of Differential Evolution （DE） algorithm， a DE algorithm based on multi-population adaptation and historically successful parameters was proposed. Firstly， all individuals were divided into elite， medium and inferior subpopulations according to fitness value， and different mutation strategies were used for different subpopulations to improve the balance between exploitation and exploration of the algorithm. Secondly， a new mutation strategy was proposed for inferior subpopulation to improve diversity of the algorithm. Thirdly， in order to further improve the balance between exploitation and exploration of the algorithm， the range of candidate parents of random individuals in each strategy was limited， which gave full play to the advantages of different individuals and the performance of the algorithm was improved. Finally， in order to strengthen the development of the algorithm， the historical successful parameters were used to guide the adaptive selection of parameters， and make the parameters keep moving in a good direction. Based on 30 test functions of CEC2014 test set， comparative experiments were carried out. Experimental results show that in 30-dimensional and 50-dimensional problems， compared with OLELS-DE （efficient Differential Evolution algorithm based on Orthogonal Learning and Elites Local Search mechanisms for numerical optimization）， the proposed algorithm has the rank level of Friedman test improved by 8.62% and 22.55% respectively. It can be seen that the performance and solution accuracy of the proposed algorithm are better， and the proposed algorithm can deal with global numerical optimization problems effectively.