Intelligent reflecting surface (IRS) is a promising candidate technology for next-generation wireless communication network which controls the propagation environment by varying the phase shift of the incoming signal. In this paper, we analyzed the energy efficiency (EE) and spectral efficiency (SE) performances of IRS-based wireless communication system (WCS). We analyze three optimization techniques that find the optimal phase shift and active beamforming (ABF) of IRS-based wireless communication such as ABF with minimum mean square error (MMSE), passive beamforming (PBF) with low complexity particle swarm optimization (PSO), and moderate complexity cuckoo search (CS) algorithms. We considered both continuous and discrete phase shift on IRS element with practical reflection coefficient model. We perform theoretical analysis and numerical simulation to evaluate the performances of the optimization techniques. The simulation results show that MMSE-based PSO algorithm converged faster than MMSE-based cuckoo search but with lower performance. Besides, the result shows that increasing minimum reflection coefficient value results an improvement on EE and SE of the system. Specifically, in practical scenario, a discrete phase shift with reflection coefficient model has significant effect on EE and SE performances. Thus, a phase shift model with a minimum reflection coefficient one have better performance than with \(0.5\) . For EE optimization, the optimal transmit power obtained is 35 dBm, and the best IRS location is found to be around \((45, 0)\text{m}\) . The SE and EE maximization of MMSE-PSO achieved \(91.23\%\) of the MMSE-CS based joint optimization in similar phase shift and \(\beta _{\min }\) value.