Evolutionary algorithms can be utilized for optimization as well as for design . Molecular docking software such as Autodock, Autodock Vina and GOLD have incorporated genetic algorithms, or methods related to genetic algorithms, into their search and optimization procedures, due to the effectiveness of these algorithms for searching chemical conformational space . The efficiency of the Lamarckian Genetic Algorithm for protein ligand docking can be improved further by using a population evolution direction-guided model . Genetic algorithms have been utilized in many applications involving the design of small molecule drugs and as well as protein drugs . Autogrow4 is a genetic algorithm based method for structure based drug design. It uses a scoring function that evaluates the binding to the specified target using Autodock Vina . Autogrow4 can use a database of fragments and a database of chemical reactions for the design of synthetically feasible molecules. In addition, Autogrow4 has several inbuilt chemical descriptor based filters for selecting molecules with desired pharmacokinetic properties. The van de Waterbeemd filter based on TPSA cutoff, included in Autogrow4 is useful for the selection of molecules for CNS targets. Earlier work has demonstrated the feasibility of modifying the scoring function of Autogrow4, with interaction fingerprints, for the design of drugs targeting a neurodegenerative disease . Autogrow4 was selected for the design of drugs to target GABA-T in this work, to utilize these features. Autogrow4 was modified in this work to guide the evolution of the population for the design of ligands with high binding affinity as well as high LogBB. This guided evolution algorithm was used for the de novo design of drugs to target GABA-T with high binding affinity and high LogBB values. This algorithm can be utilized to implement structure based drug design for other CNS disorders.