This paper presents a new optimization framework that reconciles deterministic and metaheuristic optimization in order to solve nonconvex models. In this way, it is possible to solve the convex part in a software specialized in deterministic optimization and the nonconvex part using metaheuristic tools. The chosen software for the deterministic optimization part was general algebraic modeling system (GAMS), and the metaheuristic optimization algorithm is the improved multiobjective differential evolution (I-MODE) coded in visual basic for applications (VBA). Likewise, a linking strategy is proposed through VBA code to execute the GAMS solver. The new solution strategy consists in manipulating the data that serve as parameters in the problem to be solved in the GAMS platform and evaluating the performance of the optimal values of the objective functions in the I-MODE algorithm with updated values for the involved parameters. The proposed methodology is applied to the hydraulic fracturing (HF) process to obtain shale gas. The problem related to HF process corresponds to the use of fresh water necessary for the extraction of shale gas, as well as the type of technology that can be used in the treatment of flowback water. The mathematical model that optimizes the return water management problem is solved using deterministic optimization, while the scheduling is fixed using evolutionary algorithms of metaheuristic optimization strategies. The obtained results offer attractive alternatives for the specified objective functions in an acceptable computation time.