The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed (364.8 km2) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSEQ), and especially including NSEINQ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed R2 of 0.64 and 0.55, RMSE of 0.59 and 0.58, NSEQ of 0.78 and 0.75, and NSEINQ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.