Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. To maintain a good quality of service (QoS), system administrators must provision enough resources to cope with workload fluctuations considering that resources over-provisioning reduce business profits while under-provisioning degrades performance. Existing approaches exploit automated resource scheduler to overcome these problems. The system proposes multi agent based Genetic Algorithm that learns and applies optimized resource allocations in virtualized environments to deal with the ever-changing work-load. System will takes Quality as its first consideration, and then tries to learn and apply the optimal resource allocations.