In the process of hydrological model calibration, different objective functions focus on different aspects of runoff simulation. In this paper, the HyMod model is applied to the source region of the Yellow River to explore the uncertainty of choosing objective functions and the influence of such uncertainty on runoff simulation. The Nash efficiency function f_NS, the total error of water balance function f_t, the low water error function f_d and high error function f_g are selected as objective functions. The optimal parameter sets corresponding to different objective functions are calibrated by using the genetic algorithm (GA), and are put into hydrological model in turn to simulate the hydrological processes. By comparing the yearly scale and monthly scale measured and simulated runoff processes as well as evaluation indicators such as Nash-Sutcliffe efficiency coefficient(NSE), cofficient of determination(R²) and Root Mean Square Error(RMSE), the impact of uncertainty of objective function on water resource evolution in yearly scale can be obtained. Results demonstrate that the impact of uncertainty on evaluation indicators differs remarkably when HyMod model is applied to the hydrological simulation in the source area of Yellow River in calibration and validation periods. For example, under f_NS objective function, the value of NSE is the largest, followed by f_g and f_d in sequence. In addition, the accuracy of calibration period is superior to that of verification period. Similarly, the impact of uncertainty differs in various characteristic periods. For example, under f_NS and f_t objective functions, the simulated flow values in non-flood season are respectively overestimated and underestimated. The research findings offer theoretical reference for the selection of objective functions in calibrating hydrological model parameters.