Designing new molecules possessing desired properties is an important activity in the chemical and pharmaceutical industries. Much of this design involves an elaborate and expensive trial-and-error process that is difficult to automate. The present study describes a new computer-aided molecular design approach using genetic algorithms. Unlike traditional search and optimization techniques, genetic algorithms perform a guided stochastic search where improved solutions are achieved by sampling areas of the parameter space that have a higher probability for good solutions. Moreover, genetic algorithms allow for the direct incorporation of higher level chemical knowledge and reasoning strategies to make the search more efficient. The utility of genetic algorithms for molecular design is demonstrated with some case studies in polymer design. The merits and potential deficiencies of this approach are also discussed.