In order to improve the efficiency of parameter optimization for complex hydrological models, sensitive parameters are determined through the Morris parameter sensitivity analysis. Subsequently, the multi-objective adaptive surrogate model optimization (MO-ASMO) algorithm is applied in the parameter calibration of the TOPKAPI model. The relative optimal solutions in the Pareto solution set are then selected based on the minimum Euclidean distance. The performance of MO-ASMO is compared with traditional multi-objective optimization methods, such as NSGA-II and NSGA-III, from two dimensions in terms of solution set distribution and simulation effectiveness of each flood event. The results indicate that, under the same number of model runs, MO-ASMO has a superior Pareto front compared to NSGA-II and NSGA-III. Both in the calibration and validation periods, the evaluation indicators of the MO-ASMO algorithm show better performance, overall surpassing the NSGA-II and NSGA-III algorithms, and as a result, the MO-ASMO algorithm effectively improves the efficiency of model parameter optimization.