We consider a decision-maker (DM) who can sample from multiple information sources to learn the underlying state before making an earning decision. The DM optimizes her sampling and earning decisions to maximize her payoffs. The problem is motivated by financial and healthcare applications with multiple information sources. We develop a Bayesian control framework for such problem and an efficient algorithm to solve it. Furthermore, we solve the problem explicitly in estimation and testing settings.