A key component of wildlife disease surveillance is determining the spread and geographic extent of pathogens by monitoring for infected individuals in regions where cases have not been previously detected. A practical challenge of such surveillance is developing reliable, yet cost-effective, approaches that remain sustainable when monitoring needs are prolonged or continuous, or when resources to support these efforts are limited. In order to improve the efficiency of chronic wasting disease (CWD) surveillance in Colorado, United States, we developed a weighted surveillance system exploiting observed differences in CWD prevalence across demographic strata within infected mule deer (Odocoileus hemionus) populations. We used field data to estimate sampling weights for individuals from eight demographic strata distinguished by differences in apparent health, sex, and age. In this system, individuals from a sample source with high prevalence and low inclusion probability (e.g., clinical CWD ‘‘suspects'') received ≥10.3 times more weight than those from a source with low prevalence and high inclusion probability (e.g., apparently healthy, hunter-harvested individuals). We simulated use of this alternative surveillance system for a deer management unit in Colorado and evaluated the potential effects of using biased weights on the probability of failing to detect CWD and on relative surveillance costs. We found that this system should be transparent, cost-effective, and reasonably robust to the inadvertent use of biased weights. By implementing this, or a similar, weighted surveillance system, wildlife agencies should be able to maintain or improve current surveillance standards while, perhaps, collecting and examining fewer samples, thereby increasing the efficiency and cost-effectiveness of ongoing CWD surveillance programs.