Satellite-based products can provide valuable information for model calibrations and evaluations. However, effectively and efficiently constraining hydrological models with both satellite-based information and streamflow measurements remains a challenge. Here, a parallel computing-based and spatially stepwise strategy that enables separate optimizations of different objective functions in a spatially distributed and parallel manner was proposed for model calibration with streamflow observations and satellite-based evapotranspiration (ET). The new calibration strategy (M5) was tested with the Soil and Water Assessment Tool (SWAT) model in the upper Heihe River basin of China, a mountainous watershed on the northeastern Qinghai-Tibet Plateau. The performance of M5 was evaluated and compared with two single-variable calibration strategies, i.e., streamflow-only calibration and ET-only calibration, and with two multivariate calibration methods, i.e., joint calibration and stepwise calibration. Results indicate that M5 achieves the best model performance among the five calibration strategies in reproducing temporal variations of streamflow and ET. Moreover, M5 improves the simulation of the spatial pattern of ET and attains a higher spatial efficiency than the other calibration strategies. M5 also exhibits a higher computational efficiency, with a magnitude up to 2 times greater than the other calibration strategies, due to the application of parallel computing. Further analysis demonstrates that the new calibration strategy can lead to a synergic relationship between the simulation accuracy of streamflow and ET, underscoring the added value of satellite-based products for model calibrations.