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Various meta-heuristic search methods have been employed to resolve the sensor arrangement problem, which is a type of NP-hard, combinational problem. However, the difficulty of weight tuning, when formulating a single objective function, is the chief obstacle to the use of the single-objective optimization methods. Although multiobjective optimization methods have been applied recently to avoid the difficulty involved in weight design, the original multiobjective optimization method still requires a greater number of generations for the solutions to converge to the optimal Pareto front. Moreover, unlike in previous works, we deal with four unknowns to define the sensor arrangement problem more practically: 1) The number of sensors is unknown, 2) no candidate is given for installation, 3) the coverage radii of sensors are variable, and 4) sensors cover a wide area in which obstacles exist in complicated arrangements. To improve the search approach for a sensor arrangement with these requirements, we first propose a representation scheme to encode the sensor arrangement problem as a set of chromosomes. Genetic operators and a repair scheme are also properly employed in the proposed encoding method. In addition, two strategies, i.e., the hierarchical fitness assignment strategy and the hybrid optimization strategy, are proposed to improve convergence. We also perform experiments with two commercial sensors to verify the proposed multiobjective optimization approach for sensor arrangement (MOASA). The results show that the proposed MOASA gives better performance than conventional search methods. The effects of the proposed strategies are investigated with additional experiments in terms of the quality of Pareto solutions.