In wildlife communities composed of federally endangered species, there are often several species of conservation concern that have not yet warranted federally mandated protection. These species often need continued monitoring to inform the direction of future management. While recovering endangered species is an important conservation goal, practitioners are challenged by balancing federally mandated protocols with actions that promote non-listed priority species. Practitioners need an understanding of how focused, single-species management actions may affect non-listed priority species, but developing a monitoring protocol that can detect such effects with limited resources is a challenge. Here we use constrained optimization as a path to identifying a sampling scheme that overcomes these logistical challenges and then illustrate its potential in the Sandhills region of North Carolina, USA. Using empirical results from multi-year avian community monitoring, we parameterized simulations to understand how varying the number of sampling locations and site visits affected the optimal monitoring protocol across three different avian community composition scenarios: a community with (1) 10 percent, (2) 25 percent, or (3) 50 percent non-listed priority species. We found the greatest rate of change in precision of community-level metrics such as species richness by increasing sampling replicates when surveying up to 50 sites. Importantly, this trend was apparent across all three community scenarios, indicating relatively predictable changes in uncertainty regardless of community composition. In contrast, increasing the sampling frequency did not consistently reduce uncertainty in species-level parameters such as occupancy probability. Concerningly, we saw the greatest variation when communities were comprised of 50 percent non-listed species suggesting increasingly complex monitoring protocols may be required if the number of non-listed priority species continues to increase. Practitioners could consider reducing detection error of priority species through increasing sampling frequency, as this can strongly affect optimization study designs.