With rapid growth of cloud industry in recent years, energy consumption of warehouse-scale datacenter has become a major concern. Energy aware Virtual Machine consolidation has proven to be one of the most effective solutions for tackling this problem. Among the sub problems of VM consolidation, VM placement is the trickiest and can be treated as bin packing problem which is NP hard, hence, it is logical to apply heuristic approach. The main challenge of VM consolidation is to achieve a balance between energy consumption and quality of service(QoS). In this research, we evaluate this problem and design a combined strategy using best fit decreasing bin packing method and pass-based optimization in VM placement for an efficient VM consolidation. Moreover, for maintaining energy-QoS balance, we propose a rank-based VM selection strategy and a mechanism to impose constraint on QoS to achieve desired level of performance. In addition, we propose an under-load detection method using an optimization phase. We have used CloudSim toolkit to simulate our experiments. To evaluate the performance of the proposed algorithms, we used real-world work load traces from thousand VMs and also used randomly generated work loads. Results demonstrate that our proposed methods outperform other existing methods.