Within Directed Evolution the assessment of candidate enzymes and their modification is essential. In this study we investigate Genetic Algorithms (GAs) within this context and conduct a systematic study of the behavior of GAs on 20 Fitness Landscapes (FLs) of varying complexity, which allowed the tuning of the GA to be explored. From this study recommendations for the best GA settings to use for a GA-directed High Throughput Experimental program (where populations and the number of generations is necessarily low) are reported. The FLs were based upon simple linear models and were characterized by the behavior of the GA on the landscape as demonstrated by stall plots and the footprints and adhesion of candidate solutions which highlighted Local Optima (LO). In order to maximize progress of the GA and reduce the chances of being stuck in a LO it was best to use:- 1) high number of generations, 2) high populations, 3) remove duplicate sequences (clones), 4) double mutation, 5) high selection pressure (two best individuals go to the next generation), 6) and consider using a designed sequence as the starting point of the GA run. We believe that these recommendations may be appropriate starting points for studies employing GAs within Directed Evolution experiments.