To solve Dynamic Optimization Problems (DOP), a Differential Evolution algorithm with Competitive Strategy based on multi-population (DECS) was proposed. Firstly, one of the populations was chosen as a detection population. Whether the environment had changed was determined by monitoring the fitness values of all individuals in the population and dimension of the population. Secondly, the remaining populations were used as the search populations to search the optimal value independently. During the search, a exclusion rule was introduced to avoid the aggregation of multiple search populations in the same local optimal neighborhood. After the iteration of several generations, competitive operation was performed on all search populations. The population to which the optimal individual belong was retained and the next generation's individuals of the population were generated by using the quantum individual generation mechanism. Then other search populations were reinitialized. Finally, 49 dynamic change problems about 7 test functions were used to verify DECS, and the experimental results were compared with Artificial Immune Network for Dynamic optimization (Dopt-aiNet) algorithm, restart Particle Swarm Optimization (rPSO) algorithm, and Modified Differential Evolution (MDE) algorithm. The experimental results show that the average error mean of 34 problems for DECS is less than Dopt-aiNet and the average error mean of all problems for DECS was less than that for rPSO and MDE. Therefore, DECS is feasible to solve DOP.