AbstractPump energy consumption is the major operational cost of water distribution systems (WDS). Defining better pump operation policies can reduce the cost and the enhance service provided. The nonlinear hydraulic behavior of WDS makes the pump scheduling problem complex, especially in large WDS in which several tanks and pumping stations coexist. The nonlinear hydraulic behavior of WDS is captured using numerical simulation. However, the required energy calculations were computed from the flow rate and head increase after the pumping stations. There are many optimization algorithms for the reduction of the operational cost. The majority of these algorithms are applied to hypothetical case studies with reduced feasible solution spaces. When the search space becomes large and many alternatives (pumps combinations) are possible, most of the proposed algorithms fail. Thus, the application of these algorithms in real case studies is very limited, and water utility managers still rely on human expertise to schedule pump operation rather than on automatic scheduling. This paper proposes the use of a genetic algorithm for large WDS with a many tanks and pumps. Offline hydraulic simulations allow the definition of water level bounds in the tanks. The combination of the pumps is determined a priori and the optimization algorithm is run in a reduced search space. These preparations allow the genetic algorithm to identify quickly the optimal policy and reduce the cost of energy consumption. The proposed framework was applied to the WDS of the city of Asmara, Eritrea, with 12 tanks and 9 pumping stations. The results showed that the tanks controlled by pumps worked perfectly and the water level was kept within the defined ranges. However, the constraints were not fulfilled for uncontrolled tanks. This application showed the limitations of methodologies proposed in the literature, and it is suggested that further techniques be found to schedule the pump working policies of large WDS while reducing the violations of constraints.