In urban sectors, the installation and operation of pumping stations in water distribution networks is one of the highest public costs. Therefore, some researchers have proposed different optimization models trying to simultaneously minimize investment and operating costs. One possible approach is to design each pumping station based on an optimal flow distribution. However, the problem to be overcome by this approach considers a large number of decision variables, requiring a huge computational effort. This work presents a methodology for reducing the search space of solutions by preprocessing the initial data of the distribution network, determining the maximum and minimum flow that each pumping station can supply. Our proposal focuses on identifying regions where there are no feasible solutions to the problem, avoiding the subsequent search of the optimization algorithm in these regions. Finally, the methodology is applied in some water networks to show its validity. The results show search space reductions of up to 60% in a real case study. This improves the performance of the optimization algorithm, reducing the number of iterations performed to find the final solution of the problem.