Estimation of parameters of a hydrologic model is done by a procedure called, which aims to make the model predictions as close as possible to the observed or measured values of some hydrologic variables of interest. In this study, automatic calibration of HEC-HMS hydrologic model, including a library of different event-based models for simulating the rainfall-runoff process, has been considered using particle swarm optimization (PSO) algorithm. Considering the fact that a flood hydrograph has different characteristics such as time to peak, peak discharge and total runoff volume which need to be simulated, the calibration problem has been formulated as single-objective and multi-objective optimization models. Afterwards, fuzzy set theory has been used to combine different objective functions and convert the multi-objective model to a single-objective one. Tamar Basin, a sub-basin of Golestan Dam’s Basin in north of Iran, has been selected as a real case study with four reliable measured flood events. The first three events were used for calibration and the last one for verification. As most of the models built in the HEC-HMS software are event-based, the concept of recalibration of the parameters related to a basin’s initial condition has also been introduced. Comparison of the results obtained from single and multi-objective scenarios has proven the efficiency of the proposed HMS-PSO simulation-optimization approach in multi-objective calibration of event-based hydrologic models.