Gray Code Optimization (GCO) algorithm is a deterministic algorithm based on the Gray code representation. It sometimes suffers from slow convergence and sub-optimal solutions. Expectation Maximization (EM) algorithm is used to analyze how the GCO explores the search space. The results indicate that it is similar to generating samples with a mixture Gaussian distribution. Based on these findings, a novel stochastic optimization algorithm based on the mixture Gaussian model is proposed. The new algorithm is applied to molecule conformation search. Obtaining global minimum energy conformations of molecule is a very hard optimization problem. The difficulty arises from the following two factors: the conformational space of a reasonable size molecular is very large, and there are many local minima that are hard to sample efficiently. The energy landscape in the conformational space is very rugged, and there are many large barriers between local minima.