The efficiency and installation costs of solar-powered drip irrigation systems depend on not only the efficiencies of the electrical motor, its driver, and the pump, but also the efficient usage of irrigation water. In this study, the initial installation costs and energy consumption of photovoltaic irrigation systems were decreased by obtaining the soil moisture level as a reference for optimizing energy and water consumption in a solar-powered drip irrigation system. The data from 15 moisture sensors placed in the area covered by the system were collected by a central unit using radio transmission. The soil moisture was estimated via an artificial neural network with the data obtained for micro-regions. Next, the locations of the moisture sensors in the area were optimized using a genetic algorithm to provide the optimum energy and water consumption in the system. Subsequently, the drip irrigation was controlled using moisture data from only five sensors located at the best points, as determined by the genetic algorithm. The obtained experimental results indicated that the moisture rate at the end of the period of irrigation using the system developed was more homogeneous than that of traditional irrigation systems for each micro-region using only five soil moisture sensors in a non-homogeneous area. Thus, daily energy and water consumption were decreased by 32 %, while the moisture rate in the soil was maintained within the desired range.