In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements. In order to maximize the system energy efficiency (EE) under user quality of service (Qo S) restraints in Long Term Evolution-Advanced (LTE-A) networks, which is NP-hard. , we divide it into three sub-problems to reduce computation complexity, ie, the resource block (RB) allocation, the power distribution, and the modulation and coding scheme (MCS) assignment for user codewords. and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover, a novel MCS allocation scheme is put forward, which could make make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching, and that the joint resource allocation scheme could improve energy efficiency effectively un der user Qo S requirements.