Sequential particle swarm optimization (S-PSO) is a modification of PSO suitable for high-dimensional optimization problems. S-PSO iteratively optimizes the objective function over randomly selected subspaces of the parameter search space instead of the entire parameter space at once as in standard PSO. This approach is advantageous since fewer particles are needed to solve the lower-dimension subproblems. S-PSO is applied to the distributed sonar sensor placement problem, where not only the dimensionality issue arises, but also the computational complexity of the objective function increases with the problem size. Simulations show that S-PSO outperforms standard PSO both in terms of convergence and computational efficiency.