In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as Makespan, is treated as first-class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi-objective global minimization problem with Makespan and energy consumption as the main criteria. We apply the dynamic voltage and frequency scaling model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy-aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm-based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.