Abstract: Scatter search is a newly emerging population-based evolutionary method that, unlike genetic algorithm, searches for global optima or satisfactory solutions by operating on a small data collection of intensification and diversification, and making much use of various systematic sub-method and limited use of randomization. Furthermore, scatter search uses improvement strategies to efficiently produce the local tuning of the solutions, and an extremely remarkable aspect concerning scatter search is the trade-off between the exploration abilities of the combination method and the exploitation capacity of the improvement mechanism. This paper proposes a novel uniform design and reconstructive BLX-α operator based scatter search algorithm (URBSS) to solve nonlinear continuous global optimizations based on the flexibility of scatter search, which enhances the diversification generation method used in some continuous scatter search before by introducing uniform design, and employs a reconstructive BLX-α operator, that is one of the most effective combination methods for real-coded genetic algorithms, as the solution combination method to form several offspring. Massive simulation experiments are carried out with eight popular-used testing functions, and comparison is performed against some other well-known continuous optimization algorithms presented in the literatures. From the computational results, the URBSS has proved to be quite effective and precise in identifying the global optima, and the global optimization ability and convergence rate are improved.