Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristic of the observed data deemed to be important. In this paper, a fuzzy multi-objective SCE-UA(FMOSCE-UA) optimization method for rainfall-runoff models which combines advantages of Pareto ranking and fuzzy multi-objective optimization is developed to solve the multi-objective optimization problem for hydrologic models. The FMOSCE-UA considers different aspects of the hydrograph, such as water balance, overall shape of the hydrograph, peak flow and its corresponding time, which makes the model's behavior match the observed hydrograph more closely. The features and capabilities of FMOSCE-UA are illustrated by means of a simple calibration study for Shuangpai Reservior with Xinanjiang model.