Pools of overlapping peptides corresponding to specific antigens are frequently used to identify T cell immune responses to vaccines or pathogens. While the response to the entire pool of peptides provides important information, it is often desirable to also know to which individual peptides within the pool the immune responses are directed. In this report, we analyzed various ways of deconvoluting an immune response to a pool of peptides to determine the number of different peptides to which the T cells are responding. We used a Monte Carlo simulation to optimize the construction of peptide pools that could identify responses to individual peptides using the fewest numbers of assays and patient material. We find that the number of assays required to deconvolute a pool increases by the logarithm of the number of peptides within the pool; however, the optimum configuration of pools changes dramatically according to the number of responses to individual peptides that are expected to be in the sample. Our simulation will help in the design of clinical trials in which the breadth of the response is being measured, by allowing a calculation for the minimum amount of blood that needs to be collected. In addition, our results guide the design and implementation of the experiments to deconvolute the responses to individual peptide epitopes.