Elastocaloric applications exploit the latent heat from a shape memory alloy (SMA) through its stress-induced phase transformation. The elastocaloric potential of a SMA depends on its latent heat, critical transformation stress, hysteresis, heat capacity and conductivity, and, most importantly, its cost-effectiveness. Increasing the latent heat and improving the transformation characteristics are critical to increasing the elastocaloric potential in copper-based SMAs, which depend heavily on their compositions and processing conditions. This paper reports on a comprehensive compositional optimization effort to maximize latent heat while maintaining the near room temperature transition window and minimizing hysteresis for copper-based SMAs. The effort uses a high throughput combinatorial approach to prepare and scan multiple samples with different compositions. The transformation characteristics of grouped samples were determined simultaneously using a novel differential thermal analysis (DTA) method via thermal imaging. Differential scanning calorimetry (DSC) was used to examine the down-selected compositions for verification.