In recent years, there are increasing demands in manufacturing industry for flexible production systems capable of handling high-mix, low-volume manufacturing, along with a growing population of elderly workers. As a result, reducing workers’ physical burdens and optimizing workspace layouts are urgent challenges. In particular, in repetitive tasks such as assembly and inspection performed at desks, the layout of equipment, worker posture, and movement trajectories between tasks significantly impact the operational efficiency and physical strain. Conventional methods often address these elements independently, resulting in limited effectiveness in achieving integrated spatial optimization. This paper proposes a mathematical optimization model that simultaneously optimizes worker postures, equipment layouts, and task-to-task trajectories for repetitive desk-based operations in manufacturing environments. The proposed method formulates constraints on equipment placement and worker posture by a Mixed-Integer Linear Programming (MILP) model, and integrates it with an A* pathfinding that accounts for obstacles and spatial constraints, enabling the derivation of practical workspace configurations. Furthermore, the model incorporates a dynamic optimization mechanism that reconstructs postures and paths across the entire task cycle during repetitive work, thereby aiming to balance long-term efficiency and safety. Simulation results confirm the effectiveness of the proposed method, demonstrating improvements in trajectory efficiency, reduction of postural strain, and rationalization of inter-equipment distances when compared with conventional layouts.