To solve the problems of poor performance in exploitation of the differential evolution(DE) algorithm, a new DE algorithm with fast convergence rate is proposed. Firstly, the optimal Gaussian random walk strategy is used to improve the exploitation ability of the algorithm. Then, the simplified crossover and mutation strategy based on the individuals’ optimization performance is employed to realize the evolution operation so as to improve the performance of local search. Finally, the individual selection strategy is proposed to avoid local optimum and enhance the exploration performance. Experimental results of 12 unconstrained benchmark functions and two constrained engineering design optimization problems show that the proposed algorithm is superior to the algorithm of EPSDE, SaDE, JADE, BSA, CoBiDE, GSA and ABC in terms of convergence rate, stability and convergence accuracy. The proposed algorithm can effectively enhance the exploration performance and improve the exploitation ability.