Offshore wind farms, critical for sustainable energy production, face the challenge of optimization among many parameters influencing the objectives in conflicting ways. In response, this research introduces the novel Integrative Maximized Aggregated Preference Wind Farm Optimization (IMAP-WFO) framework—a comprehensive tool designed to enhance the flexibility, accuracy, and uncertainty quantification of offshore wind farm design and operation. Existing methods often fall short due to limitations in adaptability and precision, especially when modeling complex multi-physical behaviors under uncertain conditions. IMAP-WFO represents a fundamental change by combining advanced statistical techniques and simulation methods. At its core are parametric design performance functions, capturing critical aspects of wind farm behavior, including energy production, material usage, and structural fatigue. These functions rely on Kriging meta-models, known for their accurate predictions of unknown values. To address inherent uncertainty, Monte Carlo simulations provide a probabilistic assessment of outcomes. IMAP-WFO’s true innovation lies in translating technical functions into socio-economic objectives. These include sustainability metrics, Annual Energy Production (AEP), Capital Expenditure (CAPEX), Operational Expenditure (OPEX), model uncertainty, and lifetime fatigue. Stakeholders can dynamically weigh these objectives based on their preferences, showcasing IMAP-WFO’s versatility. A validation process described in this research ensures the accuracy of design performance functions, comparing simulated results with real-world data from operational wind farms. IMAP-WFO’s application is demonstrated through case studies: optimizing the Levelized Cost of Energy (LCOE) and exploring wind farm control strategies. Overall, IMAP-WFO bridges the gap between technical challenges and socio-economic goals, helping offshore wind farm design to increase in efficiency