Satisfying non-repeated coverage, connectedness, and energy balance of sink layer are critical problems in multi-layers’ Wireless Sensor Networks (WSNs). They are overall planed as a Multi-objective Optimization Problem (MOP). For resolving it, the Quantum Wolf  Pack Evolutionary Algorithm (QWPEA) is proposed, which actualizes the Candidate Leader Wolf (CLW) selection, sliding mode crossing, quantum rotating gate, and NOR gate mutation are used to obtain the more accurate wolf’s location. Simulation results show that QWPEA can minus the number of sink nodes, promote the steadiness, and balance the energy consumption in a huge scale of WSNs effectively. While 1000 sensors are deployed on 800 m×800 m with QWPEA, the sink effective coverage ratio is higher than either MOPSO as 29.55% or NSGA-II as 25.93%. And the sink communication energy consumption ratio is higher than the latter two methods as 15.27% and 18.63% separately. Also, the sink occupied ratio is lower than them as 14.01% and 15.46% severally.