Fixture layout robust design of multistation manufacturing systems aims for an optimal design that enables the dimensional variation of a product insensitive to the variations of process variables in the manufacturing process. The robust design involves a high dimension and complex global optimization problem. Recent advances in stream of variation modeling techniques enable effective formulation of the optimization problem at the system level. However, there is a challenge of computation complexity in terms of searching optimal design parameters in a high dimension, nonconvex, and discontinuous design space. This makes many available algorithms ineffective or even invalid. In this paper, an alternative sequential space filling strategy is proposed, which adopts sampling approaches to search optimal designs. To improve computation efficiency, the search space is sequentially reduced to generate a series of subspaces, and a method is designed to ensure a complete coverage of these subspaces in the original feasible space. In order to validate the proposed method, a floor pan assembly from an automotive body assembly process is modeled, and then the fixture robust design is conducted with the developed methods. To show the effectiveness of the proposed method, genetic algorithm and sequential quadratic programming are also applied in the case study for comparison.