<p>In cluster based wireless sensor networks placement of relay nodes (RNs) as a cluster heads (CHs) with the aim of coverage of all its respective member sensor nodes (SNs) along with connectivity amongst placed CHs found to be very challenging. To form a cluster in a network each sensor node must join at least one of its nearest CH. Deployed SNs sense the targeted region within its sensing range and sends the collected data to its respective CHs. CHs collects the information from their corresponding associated SNs. These gathered data is forwarded to base station (BS) directly or through multi-hop communication for further action. Therefore, in this paper we have presented a well known meta-heuristic algorithm called Quantum inspired genetic algorithm (<i>QIGA</i>) to placed the CHs in network to deal with the following multi-objectives as, (i) to select the minimum number of optimized rendezvous/potential points (PPs) to place CHs, (ii) to provide the coverage to all the SNs by placed CHs and (iii) to maintain the connectivity amongst the placed CHs. QIGA archives the high parallel processing to the genetic algorithm to obtained the the better quality of solutions for the define objectives in lesser time. Linear programming (LP) is also formed for the define problem with suitable chromosome representation. An efficient fitness function is formulated to evaluate the chromosomes considering all the objectivities. Extensive simulation results demonstrate that the proposed QIGA-based deployment strategy significantly improves coverage ratio and connectivity degree compared to some other existing. Additionally, the simulation outcomes are evaluated through hypothesis testing, employing ANOVA and subsequent LSD post-hoc analysis. From obtained results it can be concluded that the number of selected PPs for placement of CHs by QIGA is statistically significantly lower compare to that other related algorithms.</p>