Exposure to endocrine disruptors is unavoidable. Many such compounds are suspected to impact neurologic development of children, but most studies conducted have considered effects of individual chemicals in isolation. Because exposures co-occur, it is important to consider their health impacts in a single regression framework. We applied Bayesian statistical tools (including shared mean and mixture priors for 25 unique chemicals) to study independent associations of endocrine disruptor biomarkers with autism spectrum disorder (ASD) (n = 491) and intellectual disability (n = 155), compared with 373 general population controls, in the Early Markers for Autism study. We measured biomarkers in maternal serum collected and stored from midpregnancy and considered them individually or as a class (i.e., summed polychlorinated biphenyls). We adjusted all models for original matching factors (child sex and month and year of birth), maternal age, maternal race/ethnicity, parity, and maternal education at the time samples were collected. We estimated the change in the odds of ASD or intellectual disability per 1 SD increase in the z-score of measured biomarker concentration for each chemical. Odds of ASD and intellectual disability did not change with increasing concentration for any specific endocrine disruptor. The effect estimates for each chemical were centered on or near an odds ratio of 1.00 in both models where we applied a shared mean or a mixture prior. Our mixtures analyses do not suggest an independent relationship with ASD or intellectual disability with any of the 25 chemicals examined together in this mixtures analysis.