X-Aevol is the GPU port of the Aevol model, a bio-inspired genetic algorithm designed to study the evolution of microorganisms and its effects on their genome structure. This model is used for in-silico experimental evolution that requires the computation of populations of thousands of individuals during tens of millions of generations. As the model is extended with new features and experiments are conducted with larger populations, computational time becomes prohibitive. X-Aevol is a response to the need of more computational power. It was designed to leverage the massive parallelization capabilities of GPU. As Aevol exposes an irregular and dynamic computational pattern, it was not a straightforward process to adapt it for massively parallel architectures. In this paper, we present how we have adapted the Aevol underlying algorithms to GPU architectures. We implement our new algorithms with CUDA programming language and test them on a representative benchmark of Aevol workloads. To conclude, we present our performance evaluation on NVIDIA Tesla V100 and A100. We show how we reach a speed-up of 1,000 over a sequential execution on a CPU and the speed-up gain up to 50% from using the newer Ampere micro-architecture in comparison with Volta one.