In this work, we used a genetic algorithm to discover new ground-state triplet polymers. Our data set consists of 1226 monomers, which, if thoroughly combined with each other, would create over 1.5 million (1226 2 ) potential polymers to work with. This comprehensive method can be associated with high computational resources and time. The iterative GA method can efficiently sift through the large number of combinations and produce stable ground-state triplet candidates with high confidence. Moreover, multiple GAs can run in parallel, increasing the verity of potential candidates.