Job Scheduling in Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques designed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies such as Steady State GAs and Struggle GAs. In this paper we focus on Struggle GAs and their tuning for scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures. This is particularly interesting for the scheduling problem in Grid systems, which due to changeability over time, has demanding time restrictions on the computation of planning the jobs to resources.