Offshore wind scheduling involves high cost weather constrained operations where delays are directly linked to project cost and risk. Discrete Event Simulation (DES) tools, such as Heerema Engineering Solution’s Metis are widely being used to capture uncertainty but are explored through engineering heuristics rather than optimization. This research develops a simulation-based multi-objective optimization framework and integrates the framework into Metis. This will support trade-off exploration in the installation planning phase under realistic weather and operational uncertainty. The scheduling problem presented in this research is formulated as a four objective minimization problem, using mixed-variable black-box optimization. The objective in this research are duration, cost, risk and emissions. Decision variables are presented using nine dimensional normalized encoding and deterministically decoded into physical configuration choices (e.g. yard selection and fleet sizing). Two population-based metaheuristics, NSGA-II and Multi-Objective Particle Swarm Optimization are implemented and compared under identical evaluation budgets, furthermore Monte Carlo replications are used per candidate solution. Afterwards the framework is demonstrated on the Baltyk case study: a real world offshore installation project consisting of 84 turbines, totaling to 230.400 DES runs, finally producing 74 Pareto-optimal solutions after feasibility and dominance filtering. Across the non-dominated solutions the objective ranges span 133–147 million in cost, 11.4–28.2 kt CO emissions, and 337–536 days in project duration, with an associated risk spread (𝑃90 − 𝑃10) of 30–79 days. Under the applied evaluation budget and experimental setup, NSGA-II consistently achieved higher Pareto front quality indicators than MOPSO, indicating effective convergence and diversity for this case study. The results furthermore create actionable insights such as the performance of a two barge setup, a counter intuitive benefit of the more distant yard reducing risk through weather de correlation and the dominance of May-June starting dates due to the favorable weather window