The initial study of this research applied the particle swarm optimization (PSO) heuristic to the orienteering problem (OP). PSO is a fairly new evolutionary heuristic-type algorithm created by Drs. Eberhart and Kennedy in 1995. Similar to ant colony optimization, motivation for PSO is nature-based on fish schooling and bees swarming. The OP is a variation of the well-known traveling salesmen problem (TSP) and is an NP-hard benchmark problem. Given a set of nodes with associated scores, the objective of the OP is to find a path that maximizes the total score subject to a given time (or distance) constraint. This paper presents an attractive and repulsive particle swarm optimization (ARPSO), which prevents PSO's weakness of premature convergence by maintaining solution diversity while retaining a rapid convergence. The ARPSO solves the OP with significant improvement in results when compared to PSO and is more competitive to known best published results.