To balance the global exploration and exploitation ability of differential evolution, a differential evolution algorithm based on the conjugate enhancement strategy is proposed. Firstly, a roulette-based individual selection strategy is designed according to the fitness of individual to select individuals with the poorer fitness value to form the subpopulation. Then, the temporal and spatial knowledge of the individual is used to design the conjugate enhancement direction, and the local enhancement of subpopulation is realized without losing the global exploration ability to improve the exploitation ability. Finally, experimental results of 18 benchmark functions show that the proposed algorithm is superior to the main-stream differential evolution variants and non-differential evolution algorithms mentioned in this paper in terms of computational cost, reliability and convergence speed.