The design of many real-life engineering systems involves optimization according to multiple, often conflicting, objectives. In this paper, an algorithm called spreading multi-objective particle swarm optimizer (SMOPSO) is developed and tested for optimization problems with two objectives. The motivation for SMOPSO is to promote a high diversity of solutions found in two-objective particle swarm optimization. This is attempted through the use of a spreading function based on neighboring particle positions and an archive controller which discriminates based on particle spacing. The spreading function directs non-dominated particles away from their nearest neighbor, aiming for evenly-spaced solutions as particles "spread out". To test if such an approach can indeed improve Pareto front diversity, a performance comparison of SMOPSO is made to two benchmark algorithms. Preliminary results suggest the proposed algorithm may improve the diversity of solutions for a limited selection of optimization problems, but at the expense of other important measures of performance which is discussed in this paper. SMOPSO's performance degrades for more difficult optimization problems, such those with multiple fronts and narrow global minima. An example application of SMOPSO to a theoretical, two-objective high-speed planing craft design problem is also given.