This dissertation is concerned with the unconstrained global optimization of nonlinear problems. These problems are not easy to solve because of the multiplicity of local and global minima. In this dissertation, we first study the pattern search method for local optimization. We study the pattern search method numerically and provide a modification to it. In particular, we design a new pattern search method for local optimization. The new pattern search improves the efficiency and reliability of the original pattern search method. We then designed two simulated annealing algorithms for global optimization based on the basic features of pattern search. The new methods are therefore hybrid. The first hybrid method is the hybrid of simulated annealing and pattern search. This method is denoted by MSA. The second hybrid method is a combination of MSA and the multi-level single linkage method. This method is denoted by SAPS. The performance of MSA and SAPS are reported through extensive experiments on 50 test problems. Results indicate that the new hybrids are efficient and reliable.