A sequential sampling algorithm based on Monte Carlo-based space reduction and local boundary search was introduced. This algorithm utilized the information of the current samples to reduce the design space in order to generate new samples with better space-filling and projective properties. The comparative results with existing sequential sampling algorithm demonstrate that this algorithm can efficiently generate better samples. This sequential sampling algorithm combined with Kriging model and genetic algorithm was used in the mass optimization of turbine disk

obtaining mass reduction of 10%. The results show that this sequential sampling algorithm provides a flexible and efficient approach for the engineering structure optimization.