The rapid spread of COVID-19 has resulted in over millions of confirmed and death cases, and has a huge impact on global economy as well as everyone's daily life.  In this work, we present a data-driven optimal control approach which integrates the reported partial data with the epidemic dynamics for COVID-19. This approach serves to forecast the evolution of the outbreak and provide scheduled controls of the epidemic. We provide efficient numerical algorithms based on a generalized Pontryagin Maximum Principle associated with the optimal control theory. Numerical experiments demonstrate the effective performance of the proposed model and its numerical approximations.