Java Genetic Algorithm (JGA) is a flexible object-oriented framework for rapid prototyping of evolutionary algorithms. Even though JGA has proven to be flexible and efficient in practice, parallelization opens new avenues to the framework. Java Grid-enabled Genetic Algorithm (JG<sup>2</sup>A) is a new generation of JGA that exploits parallelism in genetic algorithms in two ways: first, it allows the execution in parallel of a large set of instances (instances parallelization); and second, it provides parallelization of the population evaluation (population evaluation parallelization). We illustrate instances parallelization in different parameter tuning experiments of vehicle routing and route design problems. The population evaluation parallelization is particularly useful for hard black-box optimization problems where the fitness function evaluation embeds a discrete-event or finite-element analysis simulation. JG<sup>2</sup>A can be deployed in a heterogeneous computational environment enabled by a grid based on Globus and Condor acting as the local resource manager.