Biomonitoring studies show that humans carry a body burden of multiple classes of contaminants which are not often studied together. Many of these chemicals may be hepatotoxic. We used the 2003–2004 National Health and Nutrition Examination Survey to evaluate the relationship between alanine aminotransferase (ALT) and 37 environmental contaminants, comprising heavy metals, non-dioxin-like polychlorinated biphenyls (PCBs), and dioxin-like compounds, using a novel method. Linear regression models were constructed for each chemical separately, then as a class, using quartiles to represent exposure and adjusting for age, sex, race, income, and BMI. We then used an optimization approach to compile a weighted sum of the quartile scores, both within and across chemical classes. Using the optimization approach to construct weighted quartile scores, the dioxin like PCB, the non-dioxin like PCB and metal class-level scores were significantly associated with elevated ALT. A significant interaction was detected between the class-level score for metals, and the score for non-dioxin-like PCBs. When including all chemicals in one model, 3 chemicals accounted for 78 % of the weight (Mercury, PCB 180, 3,3’,4,4’,5-PNCB) with the remaining 22% associated with 4 chemicals (a dioxin and 3 PCBs). Validation with a holdout dataset indicated that the weighted quartile sum estimator efficiently identifies reproducible significant associations.