The wide utilization of crystal plasticity models within industrial settings is currently limited by the significant need for thorough material testing and detailed material characterization. Consequently, practical industrial setups encounter limitations in applying these comprehensive models. This work aims to efficiently ascertain model parameters by introducing a new framework centered on adaptive experimentation, which involves a minimal number of simulations to align with data from uni-axial tension tests. This strategy is highly adaptable for optimizing variables in higher dimensions, which are commonly present in crystal plasticity models. The employed Bayesian optimization technique is notably efficient and adaptive, utilizing insights from previous evaluations to carefully select parameters for subsequent assessments. By forecasting the potential ranges of parameters based on previous simulation trials, a scalable and time-effective estimation of model parameters is achieved. The suggested approach is tested on Nickel-based superalloy Haynes 282 under varied processing conditions and strain rates, utilizing a phenomenological crystal plasticity model within a calibration framework. The developed framework is proven adaptive and efficient relative to existing model calibration methods.