Phase III clinical trials are expensive, require enrolling and treating hundreds or thousands of patients at many sites. The time and cost required to do so is uncertain as is the economic value of the drug upon completion. We consider the problem of determining when and how many test sites should be opened, and the rate patients should be recruited. We model the problem as a discrete-time, discounted dynamic program with the objective of maximizing the expected net present value of a drug based on the costs of conducting the trial, and on the drug’s quality-moderated likelihood of approval and its subsequent expected revenue stream if approved. We show the optimal policy is characterized by a series of thresholds on the number of patients enrolled over time that indicate when additional test centers should be opened and how many patients should be targeted. We demonstrate using data from completed clinical trials that for lowto-moderate valued drugs, these thresholds are relevant to the firm’s decisions. We extend the problem to the case with multiple interim analyses and demonstrate that optimizing the clinical trial capacity and its utilization provides significant value in addition to the option value of stopping the trial early.