As an important element of MDO research, the search strategy for design spaces essentially belongs to the category of optimization theory. In terms of traditional single-discipline optimization problem, selecting suitable search strategies or optimization algorithms for a specific problem is a relatively mature technology. Owing to the computational and organizational complexity in MDO problems, direct use of traditional optimization methods often fails to find the optimal or near-optimal solution. Instead, the MDO problem is solved by a strategy combined with experimental design techniques and surrogate methods. Several types of search strategies are commonly used in MDO, including classical optimization algorithms, modern optimization algorithms, hybrid optimization strategies (Chen in Study on the theory and application of overall optimization design of flight vehicle. National University of Defense Technology, Changsha, 2001 [1]; Luo in Theory and application research on collaborative multi-method design of overall flight vehicle. National University of Defense Technology, Changsha, 2003 [2]; Yu in Multidisciplinary design algorithms and their application in flight vehicle design. Nanjing University of Aeronautics and Astronautics, Nanjing, 1999 [3]), surrogate assisted optimization algorithms and multimodal optimization algorithms etc. This Chapter will introduce the above search strategies respectively. Sections 8.1–8.4 mainly introduce several common search strategies. Sections 8.5 and 8.6 describe two cases of applying these search strategies to solve two specific MDO problems. Specifically, a multimodal optimization algorithm is introduced in Sect. 8.5, which achieves remarkable performance on the satellite layout optimization design problem. Section 8.6 presents a multi-method collaborative optimization algorithm for solving complex MDO problems.