Genetic algorithms (GA) have demonstrated considerable success in providing good solutions to many non-polynomial hard optimisation problems. GAs are applied for identifying efficient solutions for a set of numerical optimisation problems. Job shop scheduling (JSS) has earned a reputation for being difficult to solve. Many workers have used various values of genetic parameters. This paper attempts to tune the control parameters for efficiency, that are used to accelerate the genetic algorithm (applied to JSS) to converge on an optimal solution. The genetic parameters, namely, number of generations, probability of crossover, probability of mutation, are optimised relating to the size of problems. The results are validated in job shop scheduling problems. The results indicate that by using an appropriate range of parameters, the genetic algorithm is able to find an optimal solution faster.