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Irrigation water use is the major pressure limiting the availability of fresh water resources in the Mediterranean. Efficient irrigation scheduling programs (IRSPs) are able to reduce water consumption; however, their selection and placement in large agricultural landscapes depend on location specific characteristics and economic indicators. Towards this end, a novel and efficient Decision Support Tool (DST) is developed in MATLAB-programming, able to assess the effectiveness of different IRSPs in reducing total agricultural water use at the catchment scale along with their impact on crop yields. The DST integrates a look-up table with data on irrigation water amounts and crop yields at different locations within a catchment, populated by a hydrological and crop growth estimator: the process-based SWAT model, into a multi-objective Genetic Algorithm, which serves as the optimization engine for the allocation of measures across the agricultural land. The optimization scheme leads rapidly to the optimal trade-off frontier between the conflicting objectives providing spatial allocations of IRSPs. The tool was implemented in the Ali Efenti catchment demonstrating optimal solutions that could save more than 10% of water by reducing cotton yields less than 5% from the baseline. The study highlights the potential of the tool to assist in the development of cost-effective water saving plans at the catchment level in order to reduce the risk of desertification in intensively cultivated areas.