The intensification of agricultural activities, which are crucial for sustaining crop productivity in rural areas, can also lead to the depletion of water supplies and the degradation of water quality. The challenge of simultaneously addressing nitrate water pollution and water scarcity at a river basin scale requires the consideration of trade-offs between economic and environmental targets. The adoption of Best Management Practices (BMPs) in agriculture to address this issue is becoming increasingly widespread. This study implemented the Soil Water Assessment Tool (SWAT), a process-based river basin model that simulates hydrology, nutrient cycles, and crop growth, to evaluate nitrate discharges from agriculture to surface water and conserve irrigation water in response to alternative agricultural management practices that were accurately costed. The model was embedded within a decision support tool, enhanced by a multi-objective optimization algorithm to analyze possible alternative management schemes, suggesting the most efficient allocation of BMPs to address the above-mentioned water objectives at the river basin scale. Various BMPs, such as individual and combined changes in livestock and farming practices (including reduced fertilization, deficit irrigation, precision irrigation, cover crops, livestock stocking rate reduction, and a shortened grazing season), were explored in the Pinios river basin, demonstrating the methodology's effectiveness. The results showed that a well-balanced combination of alternative agricultural practices is very effective in reducing nitrate nitrogen by up to 13% and irrigation water consumption by up to 22% without significantly affecting profits. Among the BMPs applied, fertilization control and deficit irrigation, along with cover crops used as green manure, appeared as particularly effective. The present methodological approach can assist in the prioritization of suitable management strategies by combining a process-based hydrological model with cost estimations and an optimization algorithm, and is considered valuable in supporting decision-making aimed at the enhancement of water quality and quantity in extensively managed agricultural catchments.