AbstractGlaze ice is more likely to occur on the rotating blade, and greatly decreases the energy utilization efficiency of wind turbines. Moreover, due to its complex and irregular shape, a high-quality grid and more grid cells are needed in aerodynamic calculation. To improve this situation, this study develops a novel multiobjective optimization method for the blunt trailing edge of airfoils under glaze ice conditions. The parametric representation of the asymmetric trailing-edge profile is given by the B-spline function. The aerodynamic coefficients of the airfoils without and with glaze ice are calculated using the computational fluid dynamics (CFD) method and back propagation (BP) neural network, respectively. The update mode of the potential well center of nonoptimal particles is modified by the social learning and the optimal particle position is identified using the Lévy flight and greedy algorithm for quantum particle swarm optimization (QPSO) algorithm. The optimizer based on the improved QPSO algorithm integrated with CFD method and BP network seeks the trailing-edge control parameters maximizing the lift coefficient and lift-drag ratio. The lift and drag coefficients, lift-drag ratios, and pressure contours of the original and optimized airfoils are investigated before and after icing. Significant improvements of the aerodynamic performance are achieved in this process, confirming that the presented method constitutes a valuable tool for the airfoil design of wind turbines operating in icing conditions.