This paper proposes and evaluates novel genetic operators in a multi-objective optimization evolutionary algorithm for heterogeneous wireless sensor network (WSN) installation. To be practical on WSN deployment, the target area is divided by the installation cost and sensing coverage requirement of each region. The proposed evolutionary algorithm heuristically optimizes the sensing coverage area and the installation cost in wireless sensor networks by considering the sensor network connectivity as a constraint. The algorithm uses a population of individuals, each of which represents a set of wireless sensor nodes' types and positions, and evolves them via the proposed genetic operators. The proposed mutation and constraint-domination operators are designed to quickly seek the optimal solutions that meet the WSN installation requirements. As a result, the simulations show that the sensing coverage and the installation cost are improved. The sensor network connectivity and the sensing coverage of each region in the target area are satisfied by evolving sensor nodes' types and positions across generations. © 2013 IEEE.