The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model; secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method.