In this paper, we propose a framework for deploying and configuring a set of given sensors in a synthetically generated 3-D terrain with multiple objectives on conflicting attributes: maximizing the visibility of the given terrain, maximizing the stealth of the sensors and minimizing the cost of the sensors used. Because of their utility-independent nature, these complementary and conflicting objectives are represented by a multiplicative total utility function model, based on multi-attribute utility theory. In addition to theoretic foundations, this paper also present a hybrid evolutionary algorithm based technique to solve the sensor placement problem. It includes specialized operators for hybridization, which are problem-specific heuristics for initial population generation, intelligent variation operators which comprise problem specific knowledge, and a local search phase. The experimental study validates finding the optimal balance among the visibility, the stealth and the cost related objectives.