In face of the mounting pressure towards more sustainable chemical processes, it is increasingly important to rethink process design from the molecular level on, considering functionality along with cost and environmental aspects. Redesigning entire processes from the molecular level and in particular molecule selection have traditionally been a laborintensive, trial-and-error procedure, often constrained by time, limited chemical, and financial resources. To overcome these challenges, computer-aided molecular design (CAMD) poses a powerful tool. CAMD is a term that comprises a variety of approaches that all share the common feature to generate and evaluate a molecule's suitability for a given task using purely computational methods. The approaches used for CAMD solutions differ widely. Some approaches define their search space by screening large databases, others make use of group contribution methods, or molecules are assembled by a genetic algorithm. In general, genetic algorithms optimize a population of candidates towards a target criterion by performing genetic operations, such as crossover and mutation, to iteratively improve candidates over multiple generations. Genetic algorithms have been introduced to the engineering sciences by Holland as derivative-free methods to solve mathematical tasks and optimization problems. Through guided stochastic search, genetic algorithms improve solutions by exploring sampling areas of the search space. Genetic algorithms utilize the population to investigate various search space areas that facilitate global exploration, and 40 are thus regarded as global optimization methods, assuming that an infinite number of generations is available. In practice, a well-designed genetic algorithm typically avoids becoming trapped in local optima and often finds good and even near-optimal solutions. Venkatasubramanian et al. have first applied genetic algorithms for CAMD. Genetic algorithms have since been utilized in the design of chemicals in several different fields, including polymers, reaction solvents and drug design. More recently, Jensen has published a graph-based genetic algorithm to optimize molecules towards logP values, showing that the genetic algorithm is as good as or better than the machine learning-based approaches for certain applications in CAMD.