Water shortages have been periodically affecting the social and economic development of many regions in the world. Such effects could be mitigated by using techniques that considers the uncertainty of hydrologic variables. This paper aims at developing a model based on implicit stochastic optimization (ISO) and genetic algorithms (GA) for deriving monthly reservoir hedging rules. The ISO-GA procedure consists of optimizing the reservoir system operation under a set of possible inflow scenarios and using the acquired optimal dataset in order to construct discrete hedging rules based on GA. The proposed methodology was applied to the reservoir that supplies water to the city of Matsuyama, Japan. Based on the results, it is concluded that the devised rules are less vulnerable than the standard rules of operations during water shortage periods.