Polymer composite structures are heavily used in aerospace, defense, transport, and energy sector due to their lightweight and high-performance behavior. The behavior of these structures highly depends on curing process as it affects evolution of material properties, residual stresses, deformation, etc. Various cure process parameters, mainly temperature cycle with respect to time, need to be optimized to get the desired characteristics for these structures. In this paper, the cure process is explicitly modeled through finite element method. Its effects are captured by modeling thermo-chemical-mechanical analysis through multiple length scales. Traditional optimization techniques are time-consuming due to the unavailability of gradients, larger simulation time and exploration space. Bayesian optimization algorithms used in this study overcome these challenges pertaining to cure process optimization. Insights from such optimization can be utilized by product designers as well as manufacturers to take timely decisions to improve the performance of these composite structures.