In this paper, perhaps for the first time, a data-driven simulation–optimization model is developed based on experimental results to find the effects of state and decision variables on the optimum characteristics of a stilling basin with adverse slope and corrugated bed. The optimal design parameters of the stilling basin are investigated to minimize the length of the hydraulic jump and ratio of the sequent depths of the jump while the relative amount of energy loss is maximized. In order to model the relationship between design variables of the bed, the experimental results are converted to a data-driven simulation model on the basis of a multilayer perceptron (MLP) neural network. Then, the validated MLP model is used in a genetic algorithm optimization model in order to determine the optimum characteristics of the bed under the hydraulic jump considering the interaction between the bed design variables and the hydraulic parameters of the flow. Results indicate that the optimum values of bed slope and the diameter of the corrugated roughness (2r) can be considered as −0.02 and 20 millimetres, respectively.