An adaptive simulation–optimization (S–O) framework enables dynamic reservoir operational decision-making process during the different phases (time stages) of flood control operation during the passage of a flood event in a river–reservoir system is proposed. This is achieved by incorporating the changing priorities of the reservoir operator/manager at each phase of the flood mitigation operation into the S–O framework by evoking the appropriate set of objective functions and dynamically reconstructing the multi-objective optimization model. Five different objective functions are formulated within the S–O framework, out of which two are concerned with the mitigation at the reservoir; two more deal with the mitigation at the control point; and one ensures sufficient water is stored for meeting future demands. The non-dominated sorting genetic algorithm-II (NSGA-II) is employed to obtain the trade-off solutions from the multi-objective optimization model at each time stage. The results from the study show that the dynamic flood operation model yields a significant level of improvement in flood peak mitigation over the static model both at the reservoir as well as at the control point. The proposed S–O framework can be used in developing either deterministic or probabilistic optimal reservoir release policies for flood control operation, especially where damage functions and penalty functions are not developed.