Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the dependency between the simulation's output and the model's input is quantifed. However, classical GSA approaches, such as Sobol' indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been applied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three different metamodels for performing GSA. The micromechanical model predicts the stiffness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters affect the material properties. The obtained sensitivity analysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach.