This paper proposes an optimization framework for designing collaborative robotics cells using a digital twin during the pre-deployment phase. The approach employs Bayesian optimization to iteratively find the optimal layout, integrating production and human-centric KPIs into the design process. The digital twin enables data-driven decision-making, reduces prototype costs, and ensures continuous improvement.