In this paper, a mixed integer nonlinear model for the short-term hydropower optimization problem considering operational constraints such as demand and startup costs, is presented. The complexity of the problem is reduced by using maximum energy output rather than working with individual turbines or turbine combinations. In order to solve the model, three methods are proposed: method A, a binary genetic algorithm; method B, an iterative heuristic method; and method C, using the iterative heuristic method in the genetic algorithm. Based on computational results in a case study, method B converges to a solution very quickly and with few iterations, whereas methods A and C perform more efficiently. A comparison between methods A and C indicates that method C not only reduces the computational burden for convergence but also yields better results. The proposed methods are evaluated by comparing them with optimal solutions for the mixed integer nonlinear model. Results indicate that the proposed methods are highly effective in achieving favorable results.