Automotive topcoat curing is critical to the quality of the final coating. In production, topcoat curing quality is commonly judged through the evaluation of sample panel temperature-time series data from the finished product directly against the cure window that is specified for the polymeric coating material used. This inspection-based post-process quality control (QC) is not only methodologically passive, but also is very likely to be erroneous in its conclusions on coating quality. It can hardly generate effective guidance for quality improvement. This paper introduces a novel cure-window-based proactive QC methodology. Using this methodology, dynamic process-product models are deployed to predict the key indicators of film curing quality, according to a given cure window. A dynamic optimization method is then used to search for the optimal operational settings for film curing. The methodology can be used for the development of on-line optimal QC strategies, and it is also applicable for improving traditional inspection-based reactive QC practice. The efficacy of this methodology is demonstrated with a case study on the curing of a topcoat.