Little agreement exists on “optimal” diagnosis policies for Post-traumatic stress disorder (PTSD). The basic common presumption is to assign individuals to two groups of “with symptoms” and “without symptoms” based on screening results. Thus, current research investigates optimal diagnosis practices based on thresholds of screening results in order to maximize true positives and minimize health systems’ costs. More critically, the current diagnosis practices ignore the effects of social stigma—that is, labeling and social exclusions associated with mental illnesses—which impede patients from seeking care and can therefore hamper the outcomes of diagnosis practices. We develop a simulation model of PTSD screening in two stages. First, a static model is developed that includes screening and treatment effectiveness to study diagnosis tradeoffs based on true and false positives. Second, a dynamic model of the first model is developed and is further enhanced with the inclusion of social stigma—since stigma is an exclusively dynamic phenomenon, it is only included in the dynamic model. We then investigate optimal diagnosis thresholds in the long run. Our findings question the foundation of the current research and policy approaches to PTSD screening and diagnosis. Particularly, our analysis points to two major problems in past studies with the assumptions of isolating screening from treatment effectiveness and dichotomous categorization of target populations. We show that the current diagnosis decision making will only have short-term effects and in order to minimize stigma and the number of intense PTSD cases over the long haul, everyone in the proximity of trauma should be considered as PTSD positive, be scaled on a continuous measure, and receive early care. We present how this approach can gradually reduce social stigma surrounding PTSD.