In view of the shortcomings of basic state transition algorithm(STA) such as slow search efficiency and low convergence accuracy in the later search stage, based on the statistical study of the difference of the effects of different operators in solving specific optimization problems, a state transition algorithm with strategy adaptation(SaSTA) is proposed. Firstly, two indexes of success rate and descent rate are defined, and statistical studies are conducted on three test functions to prove the influence of different operators on the search capability of the algorithm, and an evaluation index of comprehensive success rate and descent rate is designed to adaptively select the optimal operator. Then, a nonlinear control parameter strategy is adopted to balance the exploration and exploitation ability of the algorithm. Finally, the proposed algorithm is applied to 15 benchmark functions (100, 300 and 500 dimension). The simulation results show that the proposed algorithm is superior to other comparative algorithms in terms of solution precise, convergence speed and stability.