This paper presents the MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem in hydrologic modeling. The algorithm is an extension of the successful SCE-UA single-objective optimization algorithm. The method is demonstrated through a simple hydrologic model calibration study. The multi-objective calibration problem involves minimizing multiple objective functions simultaneously, which is more comprehensive than single-objective calibration. The MOCOM-UA algorithm uses a combination of controlled random search and competitive evolution, along with Pareto ranking and a multi-objective downhill simplex search strategy. The algorithm is designed to efficiently explore the parameter space and identify the Pareto set, which represents the set of optimal solutions that cannot be improved in one objective without worsening another. The algorithm was tested on the Sacramento Soil Moisture Accounting model (SAC-SMA) using two objective functions: DRMS and HMLE. The results showed that the MOCOM-UA algorithm effectively identified a set of Pareto solutions that provided a good approximation of the true Pareto front. The study also examined the sensitivity of the results to population size and found that a population size of 500 to 1000 was sufficient to guarantee a reliable estimate of the Pareto front. The results suggest that the MOCOM-UA algorithm is a promising approach for multi-objective calibration of hydrologic models.This paper presents the MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem in hydrologic modeling. The algorithm is an extension of the successful SCE-UA single-objective optimization algorithm. The method is demonstrated through a simple hydrologic model calibration study. The multi-objective calibration problem involves minimizing multiple objective functions simultaneously, which is more comprehensive than single-objective calibration. The MOCOM-UA algorithm uses a combination of controlled random search and competitive evolution, along with Pareto ranking and a multi-objective downhill simplex search strategy. The algorithm is designed to efficiently explore the parameter space and identify the Pareto set, which represents the set of optimal solutions that cannot be improved in one objective without worsening another. The algorithm was tested on the Sacramento Soil Moisture Accounting model (SAC-SMA) using two objective functions: DRMS and HMLE. The results showed that the MOCOM-UA algorithm effectively identified a set of Pareto solutions that provided a good approximation of the true Pareto front. The study also examined the sensitivity of the results to population size and found that a population size of 500 to 1000 was sufficient to guarantee a reliable estimate of the Pareto front. The results suggest that the MOCOM-UA algorithm is a promising approach for multi-objective calibration of hydrologic models.