Particle swarm optimization (PSO), based on the idea of a swarm of birds searching for foods, is a new global optimization scheme. It can find optimal regions in searching space through the interaction of individuals in a population of particles, with the advantages of efficient searching and easy implementation. This paper deals with the inversion of the magnetic data of tabular bodies by means of PSO in comparison with the genetic algorithm (GA). The inversion results of theoretical and practical data show that PSO can find the optimal solution with fairly high efficiency.