Software development process is a series of planned activities undertaken to design a software product. The major concern in this process is estimation of cost and effort. Algorithmic as well as non algorithmic techniques are used to estimate cost and effort. Algorithmic techniques use mathematical equations; however, in case of imprecise information these techniques are overpowered by non algorithmic techniques. Intermediate COCOMO suffers from a problem of imprecise definition of cost drivers resulting in inaccurate estimations. Thus in the current research, implementation of non algorithmic modelling is carried out using soft computing techniques like fuzzy logic and genetic algorithms. The fuzzy approach is implemented to design a fuzzy model for each cost driver. The fuzzy model handles imprecise and ambiguous definition of input ranges of cost drivers. Selection of parameters characterising fuzzy sets in proposed fuzzy model is further optimized using genetic algorithms. The proposed model is tested on COCOMO NASA dataset and COCOMO NASA2 dataset using MATLAB. The improvement in performance of proposed optimized model is measured in terms of mean magnitude of relative error (MMRE) and Pred (25%). A significant improvement in %MMRE and Pred (25%) justifies the suitability of genetic algorithms for optimizing proposed fuzzy model.