The evolution of the population of designed ligands can be guided by addition of the estimated LogBB value to the fitness function used for ranking and selection in genetic algorithm based drug design methods, leading to the design of molecules with enhanced BBB permeability. Although filters can be utilized for selection after drug design using conventional methods, this work demonstrates that it is more efficient to modify the fitness function utilized for selection and ranking in the evolutionary algorithm. The method described in this work has the potential for application in the design of drugs with improved BBB permeability for other CNS disorders. The conclusions of this work may also be generalized to deduce that ligands with improved pharmacokinetic and pharmacodynamic properties can be designed efficiently by using genetic algorithms guided by fitness functions that include terms to provide a quantitative estimate of the pharmacokinetic properties of interest in addition to terms that provide a quantitative estimate of the pharmacodynamic properties.