The aim of this research is to improve the speed of the wind turbine blade optimization process while consuming less computation power. The traditional process using the constraint optimization technique and metaheuristics (MHs) cannot simultaneously handle many design variables, which is usually addressed by simplifying the problem. In this study, a novel encoding/decoding algorithm for constraint handling was constructed to handle all design variables prior to the application of metaheuristics. The performance enhancement of a 5-MW horizontal-axis wind turbine blade optimization process by the novel algorithm was tested in which 53 design variables, including chord length, twist angle, chord distribution slope, twist distribution slope, and airfoil shape, were treated and then optimized for the maximum power coefficient with several up-to-date metaheuristics. The performance gains of the metaheuristics were indicated by the highest convergence rate and minimum fitness value. Moreover, the optimal rotor had a power coefficient of up to 0.4987, 7.28% higher than that of the National Renewable Energy Laboratory offshore 5-MW baseline wind turbine. The encoding/decoding algorithm together with MHs significantly outperformed the traditional method with respect to optimization and wind turbine blade performance, thereby enabling us to complete the optimization more quickly and obtain better results.