Given limited water resources and the need to achieve both economic and environmental objectives, this study aimed to optimize cropping patterns and water allocation in the Gelvard Irrigation and Drainage Network. A multi-objective model was developed to maximize net profit and minimize water consumption. The model incorporated crop areas, water allocation, and crop yield response to water deficit using the yield response factor (Ky), and was solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The algorithm generated non-dominated solutions representing the trade-off between profitability and water conservation. Three representative solutions were selected as baseline, economic, and conservation scenarios and compared with the existing condition. Results showed that the baseline scenario, with a net profit of 23,488.6 billion Rials, water consumption of 74.57 million m³, a 9.84% reduction in water consumption, and economic water productivity of 314.99 thousand Rials per m³, provided the most balanced option. Under the conservation scenario, water consumption decreased to 59.76 million m³, representing a 27.75% reduction, although net profit declined to 16,512.5 billion Rials. The economic scenario resulted in a net profit of 22,450.1 billion Rials and water consumption of 76.14 million m³. Results also indicated substantial changes in cropping patterns, particularly reductions in rice and forage maize and increases in wheat, canola, barley, and soybean under the conservation scenario. Overall, integrating cropping pattern adjustment, targeted water reallocation, and crop-specific responses to water deficit can improve resource-use efficiency and provide management alternatives suited to different water availability conditions and