The only calibration method for Hydrologic Tank model in early days is using trialand-error. This method required much time and effort for obtaining better results since a large number of parameters need to be calibrated. Therefore, various Global Optimization Methods (GOMs) have been applied to optimize Tank model parameters automatically. In this study, genetic algorithm was introduced to auto-calibrate daily and hourly Tank model parameters. The selected study area is Bedup Basin, Samarahan, Sarawak, Malaysia. Input data used for both daily and hourly model calibration are rainfall and runoff only. The accuracy of the simulation results are measured using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2). The robustness of the model parameters obtained are further analyzed by boxplots analysis. Peak errors are also evaluated for hourly runoff simulation. Results show that GA method is able to obtain optimal values for ten parameters fast and accurate within a multidimensional parameter space that could provide the best fit between the observed and simulated runoff.