Wang (1991) reported that a genetic algorithm (GA), combined with a local search method, is an efficient and robust means for the calibration of conceptual rainfall-runoff models. In this article Wang's genetic algorithm has been slightly modified in order to improve its efficiency. The “optimal parameter set” produced by the GA has then been used as the starting point for a local optimization procedure based on Sequential Quadratic Programming (SQP). The purpose of this paper is to investigate the ability of the resulting algorithm, GA-SQP, to find the optimal parameter values during calibration of a conceptual rainfallrunoff (CRR) model. Two types of analysis were performed. The first refers to a theoretical case free of model and data errors, while the second refers to a real case in which the rainfall and runoff data were affected by evaluation errors. Specifically, in the synthetic data study, where the real set of parameters was knowna priori, a 100% success rate was observed, and in all cases the number of objective function evaluations remained relatively limited.