For Expensive Multi-objective Optimization Problem （EMOP）， although numerous related algorithms have been proposed， most existing algorithms have not achieved satisfactory results. The primary reason is that the infill sampling criteria in these algorithms fail to balance the convergence， diversity and uncertainty of selected individuals. Therefore， a Two-stage Infill Sampling-based Expensive Multi-Objective Evolutionary Algorithm （TISEMOEA） was proposed. In the first stage， a convergence-based infill sampling criterion was proposed， so as to select individuals with both good convergence and diversity， and then balance convergence and diversity. In the second stage， a diversity-based infill sampling criterion was proposed， so as to select individuals with great uncertainty without damaging convergence， and then improve the accuracy of the model and the diversity of the population. Furthermore， an adaptive diversity enhancement strategy was proposed to adjust the frequency of selecting individuals using the diversity-based infill sampling criterion， thereby enhancing population diversity and balancing exploration and exploitation capabilities of the algorithm. TISEMOEA was compared with five state-of-the-art algorithms， MOEA/D-EGO （MOEA/D with the Gaussian process model）， HeE-MOEA （Heterogeneous Ensemble-based infill criterion for MOEA）， TISS-EMOA （Two-stage Infill Sampling-based Semi-supervised EMOA）， PCSAEA （Pairwise Comparison based Surrogate-Assisted Evolutionary Algorithm）， and SFA/DE （Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems）， on the DTLZ and WFG test sets with 28 and 27 test problems， and the Inverted Generational Distance （IGD） metric was analyzed. The results show that TISEMOEA achieves the best results in 19 and 16 test problems， respectively.