Offshore wind farms, critical for sustainable energy production, face the challenge of optimization among many parameters influencing key performance indicators in competitive ways. This research introduces the novel Integrative Maximized Aggregated Preference Wind Farm Optimization (IMAP-WFO) framework – a comprehensive tool designed to enhance flexibility, accuracy, and uncertainty quantification in 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 overcomes these limitations 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. 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, including sustainability metrics, annual energy production, capital expenditure, operational expenditure, model uncertainty, and lifetime fatigue. Stakeholders can dynamically weigh these objectives based on their preferences. A validation process ensures the accuracy of design performance functions, comparing simulated results with real-world data. IMAP-WFO's application is demonstrated through case studies: optimizing the levelized cost of energy and exploring wind farm control strategies.