Increasing water scarcity and competition among different water-use sectors have highlighted the need for advanced optimization approaches to simultaneously manage cropping patterns and irrigation scheduling in agricultural systems. This study aimed to determine the optimal cropping pattern and irrigation schedule to maximize net profit and improve economic water productivity in Unit 1 of the Tajan Irrigation and Drainage Network, Sari, Iran. Seven crops, including rice, citrus orchards, wheat, barley, canola, forage maize, and soybean, were evaluated across a cultivated area of 3,000 ha. An optimization model was developed to maximize net profit under constraints related to water availability, cultivable land, and management requirements, and the Grey Wolf Optimizer (GWO) was employed to solve the model. To evaluate different water management strategies, 27 scenarios were developed by combining three water supply levels (100%, 80%, and 60%), three irrigation intervals (5, 7, and 10 days), and three Management Allowable Depletion (MAD) levels (100%, 80%, and 60%). The results showed that integrated management of cropping patterns and irrigation scheduling can reduce water consumption without substantially reducing economic returns. The best-performing scenario, W60-I10-M60, reduced irrigation water consumption by 7.87% compared with the baseline scenario, while increasing net profit by 0.35% and economic water productivity by 8.93%. Overall, the results indicate that metaheuristic optimization models can serve as effective decision-support tools for water allocation, irrigation management, and improving the economic sustainability of irrigation systems under water-scarce conditions.