Floating offshore wind energy is a technology that has gained significant interest over the past few years due to its potential to unlock vast, high-quality wind resources far from shore and in deeper waters, areas that fixed-bottom turbines cannot reach. However, harnessing this resource presents unique challenges, among them are maximising annual energy production (AEP) while minimising blade fatigue damage. These objectives inherently conflict. Aggressive control settings that boost energy capture tend to increase loads, accelerating material fatigue. Crucially, both AEP and fatigue life depend on the turbine controller. By systematically varying a set of key features of a proportional–integral (PI) controller, this thesis sets up a workflow to investigate how controller parameters shape the trade-off between AEP and blade fatigue. To navigate this multi-objective landscape, Pareto optimisation is employed, generating a front of controller configurations that balance energy yield against blade durability. Economic viability is assessed through levelised cost of energy (LCOE) calculations, linking extended fatigue life to deferred maintenance costs and potential improved lifetime. Within the existing literature, where past studies have treated AEP, fatigue, and control in isolation, this work offers a unified framework, demonstrating how slight adjustments in controller settings can unlock significant performance gains. The core contribution of this thesis lies in the framework itself. An end-to-end roadmap is presented, combining metocean data analysis from Utsira Nord, aero-servo-hydrodynamic simulations using SIMA, AEP analysis and fatigue life estimation, Pareto front construction, and LCOE impact assessment. Key results show that blade life can be extended by over 15% with less than a 1% drop in AEP, translating to a meaningful reduction in LCOE of at least 3% under typical economic assumptions. These findings highlight the opportunity to explore the controller parameter space further with this system, paving the way for more finely tuned strategies that optimise the balance between the two objectives.