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Fundamental weakness of the tank model application is so much value parameters must first be defined simultaneously before the model was applied. This condition causes tank models are considered not efficient to solve practical problems. This research is an attempt to improve the performance of Standard Tank Model that can be applied more effectively, especially for the transformation of climate data into the stream data. The discussion focused on efforts to complete the system of equations in standard tank model using genetic algorithms for optimization parameters, so that the resulting equation system can determine the appropriate model parameters automatically at a watershed in the study. Standard tank model is a system composed tank 4 series and has 17 parameters. Results of research on the Konto Watershed and the Lekso Watershed show that Standard Tank Model-based Genetic Algorithm can present relationships very well climate data and streams data. At the maximum generation value of 500 obtained root mean square error (RMSE) of 0.241 m3/sec for the Konto Watershed and the Lekso Watershed of 0.30 m3/sec. Keywords: genetic algorithm, a standard tank model, optimization, parameters

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