Artificial Intelligence (AI) is the fastest-growing area in recent years to support decision-making but it may also involve environmental impacts and energy implications. Researchers emphasize the importance of considering energy consumption to ensure sustainable AI solutions. This paper examines how evolutionary algorithms (EA), specifically genetic algorithms (GA), affect energy consumption when applied to optimization problems. It compares GA implementations in C++ and Python, analyzing the impact of parameters such as population size and the number of generations across several GPU and CPU architectures. Results showed that using a GPU for parallel processing with different values of population size does not always mean that we will have great energy savings, especially if the GPU used is not designed to minimize the consumption of unused resources. However, if we look at the instantaneous consumption, small differences can be seen for large population sizes. Interestingly, smaller populations sometimes require higher energy consumption. Regarding programming languages, the instantaneous energy consumption on the CPU is directly related to the population size. On the GPU, the energy performance with C++ is consistently lower than with Python, clearly demonstrating that utilizing modern technologies results in reduced energy consumption.