Numerous methods have been developed for testing Connected and Automated Vehicles (CAV). The scenario-based approach is considered the most promising as it reduces the number of scenarios required to certify the CAV system. In this study, we propose a refined six-step methodology that includes two additional steps to compute a critical index for scenarios and use it to guide the sampling process The methodology starts with the generation of functional scenarios using a 5-layer ontology. Next, the driving data is processed to determine the criticality indices of the functional scenarios. This is achieved by using a latent Dirichlet Allocation technique and a Least Means Squares method. Finally, the sampling process is built on a scenario reduction based on clustering and a specific metric related to the a priori criticality indices. Overall, our refined approach enhances the scenario-based methodology by incorporating criticality indices to guide the sampling process, which can reduce drastically the number of scenarios needed for certification of CAV systems.