The appropriate use of a conceptual rainfall-runoff model depends on how well its parameters are calibrated. Generally, rainfall-runoff models deal with numerous parameters that cannot be measured directly and should be estimated through optimization tools. The purpose of the optimization approach is to finalize the best set of parameters associated with a given calibration data set that optimize the evaluation criteria. In this paper, a continuous genetic algorithm calibration method has been used to estimate the NASH conceptual model parameters (n, k).The efficiency of the method was evaluated using the estimated parameters to simulate different rainfall - runoff events that happened in the Kasilian watershed in Mazandaran province during previous years. The calibration and validation results have shown that the suitability and efficiency of this model for auto calibrating of Nash conceptual rainfall-runoff model is more accurate and favorable.