The accuracy of rainfall runoff modeling plays a crucial role in the simulation and evaluation of hydrological processes. Numerous physical parameters need to be determined when modeling using SWMM. Automatic calibration helps to achieve improved model accuracy. However, the SWMM model does not have automatic way of calibration. For this purpose, this study is dedicated to combining the NSGA II optimization algorithm with pyswmm to achieve automatic parameter calibration for the swmm model. The swmm model was calibrated and validated using rainfall and runoff measurements from a monitoring catchment located in Taipei City, Taiwan. The results of the study show that the calibrated parameters help to improve the accuracy of the SWMM model quickly compared to relying on manual determination. The relative error of the peak flow rate is lower than 10%. However, the baseflow has a significant impact on the validation of the results. Based on the current research results, it is expected that the proposed method will be exerted on other similar environmental modeling problems.