A laser multilateration network is a large-scale coordinate measurement network based on the multi-station measurement by laser trackers. The locations and number of trackers, i.e. the network placement, strongly influence the performance of this measurement network. Thus a grid-based placement optimization method is proposed based on the genetic algorithm, in which the coverage capability, measurement accuracy and overall cost are used as the multi-objective evaluation functions, while the global grids and local grids are used for the dividing of the placement area. Through the global search and local search, a global optimization can be achieved without any initial placements. The experimental results show that compared with the experience placement, the optimized network placement can meet the measurement requirements easily and reduce the requirement of operation experiences from operators. Thus it possesses a good practicality.