This article focuses on the use of genetic algorithms in developing an efficient optimum design method for tiltingpad bearings. The approach optimizes based on minimum film thickness, power loss, maximum film temperature, and a global objective. Results for a five tilting-pad preloaded bearing are presented to provide a comparison with more traditional optimum design methods such as the gradientbased global criterion method, and also to provide insight into the potential of genetic algorithms in the design of rotor bearings. Genetic algorithms are efficient search techniques based on the idea of natural selection and genetics. These robust methods have gained recognition as general problem solving techniques in many applications.Many numerical optimization methods have been developed and used for design optimization of journal bearings. Most of these methods are based on gradient techniques. These methods are reasonably effective for well-behaved objective functions. This is because the gradient of the function helps to guide the direction of the search. However, when the continuity and existence of derivatives of the function are not assured, gradient methods lack robustness and may trap in local optima. To overcome these problems, many different approaches exist in the literature. The development of faster computers has allowed development of more robust and efficient optimization methods. One of these robust methods is the genetic algorithm, which has gained recognition as a general problem solving technique in many applications. The genetic algorithm is a guided random search technique. It uses objective function information, instead of derivatives as in gradient-based methods.Numerical search techniques are good at exploitation but not exploration of the parameter space. They focus on the area around the current design point, using local gradient calculations to move to a better design. Since there is no exploration for all regions of parameter space, they can easily be trapped in local optima (Davis, 1991). Genetic algorithms are a class of general-purpose algorithms that can achieve a "remarkable balance between exploration and exploitation of the search space" (Mitsuo and Runwei, 1997). The genetic algorithm is new to the field of journal bearing analysis, and in current literature there is limited work in the area of rotor-bearing system using genetic algorithms. Interested readers can refer to the studies by