The purpose of this study is to develop an effective method for balancing garment assembly line to improve efficiency, reduce bottlenecks, and minimize non-value-added time. The research addresses limitations in manual task allocation by using Ranked Positional Weight (RPW), evolutionary optimization techniques for dynamic workstation planning, and simulation. A jacket sewing line consisting of 27 operations was selected for analysis using RPW and allocated using a custom Python algorithm under Basic Pitch Time (BPT) constraints. Standard Minute Values were measured through time studies, and precedence relationships were constructed. Two evolutionary optimization methods – Genetic Algorithm (GA) and Differential Evolution (DE) were applied to increase workload balancing, and the proposed layout was validated using simulation. DE achieved a more compact layout with 16 workstations and higher line efficiency (77%) compared to GA's 18 workstations and efficiency (71%). GA resulted in slightly higher total output (509 units) over DE (488 units) in 10 hours and maintained full compliance with precedence constraints, while DE had faster convergence but introduced minor violations. DE also had lower SMV variance, indicating better workload distribution. The proposed method provides an integrated method that was validated through simulation and offers a practical approach for balancing and optimizing line efficiency.