Aiming at the energy-efficiency coverage optimization problem of wireless sensor networks under the uncertain environment, an interval sensing model for the sensor node is constructed. Subsequently, it is converted to an interval multi-objective optimization problem by taking the coverage rate and the node’s redundancy rate as two objectives. A multi-objective quantum cultural algorithm with interval parameters is proposed based on a novel dominance relationship derived from the possibility degree, which is used to compare two interval individuals. In the belief space, the implicit knowledge extracted from non-dominated individuals is used to update the quantum individuals and guide the mutation or selection operation of the evolutionary individuals. The simulation results under various environments show that the optimal Pareto front obtained by the proposed algorithm has better convergence, uniformity and scalability. Corresponding wireless sensor network’s layouts are more reasonable.