Power production from wind energy has been increasing over the past decades, with more areas being used as wind farms and larger wind turbines (WTs) being built. With this development, awareness of the impact of wind energy on the environment and on human health has also raised. There has been a large interest in developing fast turnaround WT blade design frameworks, capable of predicting both aerodynamic and aeroacoustic performance to handle ever stricter noise criteria constraints dictated by site or local authorities. In this work, a blade element momentum theory model is used to predict the aerodynamic performance of a wind turbine, coupled to an empirical aeroacoustic noise model and boundary layer corrections. The aeroacoustic prediction code developed was validated against measurement data of the AOC 15⁄50 WT and included in an optimization framework using a genetic algorithm. The blade shape was parametrized using NURBS curves for the cross sectional airfoil shapes and Bézier curves for the twist and chord distributions, totaling up to 62 design variables. Two multi-objective optimization cases, both single- and multi-operating point, were performed. Optimal solutions selected from the Pareto fronts are discussed in detail. These solutions ranged from an increase in annual energy production of 15 % to a reduction in noise levels of 9.8 %. It was demonstrated that substantial noise reduction could be obtained at an expense of a minor aerodynamic penalty.