With growing awareness of environmental sustainability in the fashion industry, waste reduction in apparel production presents an opportunity for innovation. Existing patternmaking methods for waste reduction are typically time consuming and departs from existing design practices. This study attempted to mitigate these issues by developing a program that takes existing patterns of conventional clothing designs and uses the slash patternmaking method to generate a reduced waste version. Following the engineering design process and life cycle assessment framework, this study proposed a reinforcement learning approach to slicing and packing patterns. Results show that the reduced waste designs significantly increased fabric utilization. Following the development of the program, this study sought to evaluate consumers’ responses to these generated designs. Based on these three basic designs, three derived reduced waste designs were generated using the program. Then, 103 participants were surveyed for their opinions. For all three styles, participants reported that they were willing to accept the reduced waste versions despite design changes and their associated price increase. Finally, this study evaluated the tool through workshops with fifteen professionals and students. Participants reported that the tool was easy to use and were generally accepting of the design changes. Overall, this study presented a novel computational approach to waste reduction in clothing patternmaking.