Due to high electricity consumption in the Cloud datacenters, providers aim at maximizing energy efficiency through VM consolidation, accurate resource allocation or adjusting VM usage. More generally, the provider attempts to optimize resource utilization. However, while minimizing expenses, the Cloud operator still needs to conform to SLA constraints negotiated with customers (such as latency, downtime, affinity, placement, response time or duplication). Consequently, optimizing a Cloud configuration is a multi-objective problem. As a nontrivial multi-objective optimization problem, there does not exist a single solution that simultaneously optimizes each objective. There exists a (possibly infinite) number of Pareto optimal solutions. Evolutionary algorithms are popular approaches for generating Pareto optimal solutions to a multi-objective optimization problem. Most of these solutions use a fitness function to assess the quality of the candidates. However, regarding the energy consumption estimation, the fitness function can be approximative and lead to some imprecisions compared to the real observed data. This paper presents a system that uses a genetic algorithm to optimize Cloud energy consumption and machine learning techniques to improve the fitness function regarding a real distributed cluster of server. We have carried out experiments on the OpenStack platform to validate our solution. This experimentation shows that the machine learning produces an accurate energy model, predicting precise values for the simulation.