When considering an optimisation problem with Evolutionary Algorithms, two main measures are taken into account to understand its performance: the quality of the solution and the computation time. Both values are used to compare the performance of different versions of an algorithm, algorithm parameter tuning or even to compare EAs with other available heuristics. Although the quality of the solutions and the execution time are usually the main characteristics of the algorithms, recently a new trend in computer science tries to contextualise them under a new perspective: the energy consumption. This paper presents a preliminary analysis of the standard genetic algorithm, using two well-known problems and considering solution quality, computation time and energy consumption when using battery-powered devices to run them. The results show that some of the main parameters of the algorithm have an impact on the instantaneous power consumption - which is far from the expected behaviour - and therefore on the amount of energy needed to run the algorithm. Although we are still far from finding a way to design energy-efficient Evolutionary Algorithms, we consider that the results open a new perspective that will allow us to reach this goal in the future.