User frustration is one negative consequence of human–computer interaction caused by bad interpretations and insufficient adaptation to user preferences. In this scope, genetic algorithms (GAs) might offer some insights to mitigate this problem. Hence, we conducted a systematic review to identify the implementation of GAs in the field of the design of conversational agents (CAs). Our results displayed that the literature focuses on three clusters mainly using evolutionary algorithms, and binary-coded GAs for natural language processing (NLP).