Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve hydrological modeling efficiency and performance with high reliability. To evaluate the performance, the proposed MS-CUA method was applied to two case studies comparing two traditional methods: sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method can quickly locate the highest posterior density (HPD) regions of parameters to improve computational efficiency. It also provided better-calibrated results and more balanced uncertainty analysis results comparing with other traditional methods.