Efficient scheduling of work orders in weaving looms is crucial for improving production efficiency and meeting tight delivery deadlines in the textile industry. This study proposes a genetic algorithm (GA)-based model to optimize work order assignments, minimize type changeover durations, and balance machine workloads. The model uses real-world ERP data, supports job splitting for parallel production, and dynamically classifies type changes into variant, linked warp, and full setup changes. Experimental results show significant improvements in planning time, changeover reduction, and delivery performance. The proposed GA approach offers a scalable and intelligent solution that can be readily adopted for modern textile manufacturing challenges.