This research aims to improve the design of attribute control charts, focusing on the Cumulative Sum (CUSUM) chart, which is crucial for quality management in industries. Despite the easier management of attribute quality characteristics, there is limited study on their economic-statistical design. Building on Duncan’s model, this study introduces an optimized approach for CUSUM control charts to efficiently monitor defect rate changes. The optimization combines economic assessments of assignable cause costs with statistical enhancements to the chart’s detection speed. The goal is to lower the Expected Total Cost (ETC) while keeping false alarm rates and inspections within set limits. This approach is expected to increase process efficiency, reduce quality costs, and ensure quality. A responsiveness analysis for the optimized chart against traditional methods is conducted, considering various costs, designs, and shifts. The optimized CUSUM chart is found to offer better economic and statistical performance, providing a superior tool for quality control in statistical process management.