Location area (LA) management is a very important problem in mobile networks. In general, registration and paging costs are associated with tracking the current location of a mobile user. Considering minimizing the total of paging and registration costs as the main objective, the aim is to provide corresponding cell-to-switch and cell-to-LA assignments. This paper compares three well-known evolutionary algorithms to measure their suitability for solving location area management problems; these are genetic algorithms, multi-population genetic algorithms and memetic algorithms. To handle multiple objectives of paging and registration, a two-stage multi-population CA is developed. A memetic algorithm is introduced in order to improve the performance of a CA with the local search techniques. The effectiveness of these methods is shown for a number of test problems with different network size and characteristics.