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Randomized experiments can provide unbiased estimates of sample average treatment effects. However, estimates of population treatment effects can be biased when the experimental sample and the target population differ. In this case, the population average treatment effect can be identified by combining experimental and observational data. A good experiment design trumps all the analyses that come after. While most of the existing literature centers around improving analyses after RCTs, we instead focus on the design stage, fundamentally improving the efficiency of the combined causal estimator through the selection of experimental samples. We explore how the covariate distribution of RCT samples influences the estimation efficiency and derive the optimal covariate allocation that leads to the lowest variance. Our results show that the optimal allocation does not necessarily follow the exact distribution of the target cohort, but adjusted for the conditional variability of potential outcomes. We formulate a metric to compare and choose from candidate RCT sample compositions. We also develop variations of our main results to cater for practical scenarios with various cost constraints and precision requirements. The ultimate goal of this paper is to provide practitioners with a clear and actionable strategy to select RCT samples that will lead to efficient causal inference.