Mass screening is an essential tool that is widely utilized in a variety of settings. The objective of mass screening is to maximize the overall classification accuracy under limited budget. In this work, we address this problem by proposing a proactive optimization-based framework that factors in population heterogeneity, limited budget, different testing schemes, the availability of multiple assays, and imperfect assays. By analyzing the resulting optimization problem, which is a mixed integer nonlinear programming problem, we establish key structural properties which enable us to develop an efficient solution scheme.