Data collection planning is an important step in the experimental design process. The context in which we address data collection planning is that of collecting second-stage data in system reliability studies. This problem is often referred to as resource allocation. We motivate this problem by summarizing a Bayesian hierarchical model for assessing the reliability of a system and the current approach used to find an optimal resource allocation; this approach is computationally intensive, requiring repeated analyses via MCMC. We then introduce a computationally efficient approach for evaluating candidate resource allocations. Our approach can easily be combined with an optimization procedure to search for an optimal resource allocation. Specifically, we describe how genetic algorithms can be used to search for optimal resource allocations. We demonstrate the usefulness of our approach by employing genetic algorithms to search for an optimal resource allocation for collecting additional data to assess the reliability of an air-to-air heat-seeking missile and the reliability of a low-pressure coolant injection system, a safety feature in some nuclear-power boiling-water reactors.