Production scheduling in multi-machine environments often requires balancing two competing priorities: meeting a target completion sequence and minimizing setup or changeover time, particularly when product colors drive significant setup costs. This study presents a desktop scheduling system that integrates a Genetic Algorithm (GA) with a constraint-based schedule builder to generate feasible production plans from an Excel input dataset. The system validates required fields, models setup times using three operational conditions (initial setup, same-color setup, and different-color setup), and automatically converts multi-color items into sequential job segments to reflect color-step execution. To support varying operational goals, the objective function is user-configurable through a mismatch-versus-setup weighting, enabling planners to emphasize either adherence to the desired output order or reduction of setup time. Results are delivered through a tabular schedule view and Gantt visualizations, and an interactive “What-if” module allows controlled manual adjustments while blocking invalid edits such as overlaps or incorrect segment ordering. Experimental evaluation on the fixed dataset demonstrates that the GA-based approach consistently produces feasible schedules and provides practical trade-offs between sequence accuracy and setup efficiency. Comparisons with alternative optimization methods further indicate a quality–runtime feasibility trade-off, supporting the proposed GA framework as a balanced solution for this scheduling context.