Design decisions during New Product Development are often made with less than the ideal information regarding the design alternatives. Such decisions may result in unplanned costs during Product Development, delays in Start-of-Production, and increased warranty costs. As a result it becomes necessary to formulate a strategy that ensures the appropriate amount of information will be available in order to support an optimized decision; where such a strategy may exist as the design of a reliability test or development of a FEA plan. The challenge with these strategies comes in the way of quantifying the value of information that can be gained through the investment of facilitating these activities. Often statistical methods are utilized to determine sample sizes for comparative studies, but these methods require assumptions that may not necessarily support the situation of the Product Development effort. First and foremost is the assumption of independence. That is to say, the competing design alternatives being evaluated are assumed to be independent of one another; however, most often the competing alternatives share some level of common technology that prevents the assumption of independence from being satisfied. This paper will formulate a framework for determining the optimal level of information needed to maximize the utility of the decision. The approach to be presented will take into consideration competing design alternatives that, at some level, are correlated among one another. The approach utilizes Decision Analysis and the theory of Expected Utility and Certainty Equivalent to support the Decision Maker's efforts in developing strategies for evaluating correlated Design Alternatives. The approach will utilize Bayesian methods to incorporate the prior belief of the Decision Maker's perception as it is related to the reliability of the different design alternatives. A simulation-based algorithm will be used to examine a random field of potential failure probabilities and failure counts, which are then used to update the prior belief as an estimate a posterior density of the failure probabilities. The posterior estimate will be used to obtain the expected utility of the decision. This algorithm will be used to evaluate different levels of investment and provide a suggestion for the optimal investment, e.g. observation size. A specfic example will be presented where the Decision Maker is to develop a research effort to evaluate the failure probability of three different design alternatives. An assumption on the prior densities for the failure probabilities will be made, as well as an estimate in the level of correlation that exists among the failure probabilities of the design alternatives. A copula will be used to establish a joint density on the failure probabilities and a MonteCarlo simulation will be used to sample failure probabilities from the joint density, as well as potential realizations on the number of failures from a target observation size. The prior densities will be updated, i.e. a posterior density will be denied and an estimate of the Expected Utility and Certainty Equivalent will be made. In addition, the approach will present the application of the Metropolis-Hastings algorithm as a means to circumvent the complex integration that are part of the Baye's Formula or Baye's Rule. This approach is intended to reduce the overall simulation time.