To explore the feasibility of introducing remote sensing soil moisture data to assist in distributed hydrological model parameter calibration in semi-arid small watersheds, a joint parameter calibration method that integrated hard data (flow rate) and soft data (remote sensing soil moisture) was proposed. CLDAS satellite remote sensing soil moisture data were applied to the Grid-Multi-GA model. A multi-objective optimization framework was adopted, and the Nash efficiency coefficient of flow rate simulation and the Spearman correlation coefficient of soil moisture’s spatiotemporal distribution were used as dual evaluation indicators. By adjusting the weights of the system to dynamically balance two types of indicators, the optimal weight and its corresponding runoff generation and flow routing parameter combination were ultimately determined. To validate the feasibility of the joint calibration method, three model scenarios were established, namely uncalibrated parameters, calibration using only discharge, and joint calibration of discharge and soil moisture. The case study results in the Ningxia Yuanzhou Watershed demonstrate that the Grid-Multi-GA model jointly calibrated by flow rate and soil moisture achieves a Nash efficiency coefficient greater than 0.7 and a Spearman correlation coefficient of 0.84 in flood simulation for small watersheds, significantly outperforming both the uncalibrated model and the model calibrated only with flow rate.