To solve the problem that the surrogate is usually limited to a single type, a multi-objective surrogate-based optimization method based on general improvement decomposition strategy is proposed. In this method, the model prediction value information is fully used to construct the general improvement multi-objective decomposition criterion and the general improvement R2 indicator criterion, thus expanding the selection scope of surrogate models in multi-objective surrogate-based optimization methods. The two proposed criteria can achieve an adaptive balance between global exploration and local exploitation with random uniform weights. Comparison results show that the proposed method has good optimization performance under limited simulation conditions, and the Pareto front has certain advantages in convergence, diversity, and spatial distribution. Compared with similar methods, the proposed method has the following advantages: 1) it is suitable for many different surrogate-based optimization methods because the uncertainty of the model prediction is unnecessary, 2) its implementation is simple and computational complexity is low, which can significantly improve the optimization efficiency of expensive black-box problems.