The physically realistic functions implemented in nearshore process models are governed by parameters that usually do not represent measurable attributes of the nearshore and, therefore, need to be determined through calibration. The classical approach to calibrate nearshore process models is via manual parameter adjustments and visual comparisons of model predictions and measurements. In this paper a hybrid genetic algorithm, comprising a global population-evolution-based search strategy and a local Nelder–Mead simplex search, is used to calibrate nearshore process models in an objective and automatic manner. The effectiveness of the algorithm to find the optimum parameter setting are examined in two case studies with increasing complexity: a simple alongshore current model and a more complex cross-shore bed evolution model. Whereas the algorithm is found to be an effective method to find the optimum setting of the alongshore current model, it fails to identify the optimum parameter values in the bed evolution model, related to the strong interaction between two of the parameters in the suspended sediment transport equation. Setting one of the interdependent parameters to a constant value within its feasible space while retaining the other in the optimization procedure is found to be a feasible solution to the ill-posed optimization problem of the bed evolution model.