Artificial intelligence (AI) is advancing quickly, but its widespread use raises environmental concerns due to the high energy and water consumption of AI systems, particularly during training and large-scale operations. Green AI has emerged as a solution, focusing on specialized algorithms and infrastructure to lessen these environmental effects. Although the environmental footprint of standard AI methods is understood, optimization algorithms, notably Genetic Algorithms, have received less attention despite their frequent application in AI. In this study, the carbon efficiency of different combinations of crossover-mutation operators in a real genetic algorithm (rGA) was examined. The emissions were measured across a range of settings, finding consistent patterns in emissions ranging from 2.669E-05 to 2.204E-04 kg CO \(_2\) eq. While small in scale, these values can add up to significant costs in larger experimental or real-world scenarios. Statistical tests confirmed significant differences; combinations such as 1P_LM (one-point crossover and boundary mutation), 2P_LM (two-point and boundary mutation), and UNI_LM (uniform crossover and boundary mutation) consistently yielded the lowest emissions.