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Optimal design of chute-flip bucket (CFB) system depends on various parameters, among which energy dissipation and cavitation prevention are the most important. This study develops a simulation-optimization model based on a calibrated Flow-3D numerical model, multi-layer perceptron artificial neural network (MLP-ANN), and genetic algorithm (GA) optimization approach for determining the optimal geometry of the CFB system. To alleviate the computational time burden of the Flow-3D numerical model, a MLP-ANN meta-model is developed based on some limited simulations of Flow-3D. The meta-model framework is then coupled with GA to provide an efficient design framework for the CFB system. The proposed framework is employed to design optimal geometry of the CFB system of the Jareh dam in Ahvaz, Iran. The results show that the obtained optimal design increases the cavitation index up to 30% and energy dissipation up to 32% compared to the old engineering design already in place.