A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. Especially, Medical knowledge consists of a combination of structural information about known biological facts and probabilistic or actuarial information about exposures to hazards and recovery rates. Probabilistic information is especially difficult to use, as it requires constant maintenance and it usually comes in the form of study results which are not ideally suited for making individual predictions. Patterns summarizing mutual associations between class decisions and attribute values in a pre-classified database provide insight into the significance of attributes and are also useful in classificatory knowledge. The proposed work is an efficient method to extract significant attributes from a database. Reducing the features or attributes enhances the quality of knowledge extracted and also the speed of computation. In this paper the design of a hybrid algorithm for heart disease diagnosis using effective and efficient genetic algorithm and fuzzy logic is implemented. The proposed work analyses the time complexity of genetic-fuzzy system.