This paper presents a novel approximate dynamic programming technique for solving the pump schedule optimization of real-water distribution networks. The method is based on the significant decreasing of the search space by splitting the optimization problem into smaller units. In addition, the state space of the main distribution system was further reduced to the most important reservoirs. The capabilities of the proposed technique are demonstrated on a real-life problem, the water distribution system of the town of Sopron, Hungary. Nine test cases were defined which represent different initial water level scenarios, thus the new application was easy to compare to a former developed genetic algorithm and to some world-leading optimization solvers which are available on the NEOS Server. The benefits and drawbacks of these deterministic and heuristic methods are highlighted.