One of the most important effective factors the software companies face is the Software Cost Estimation (SCE) in software development process time. SCE is one of the subjects which have been considered in late decades in many researches. The real estimation in software development needs effort and cost factors which are done by use of the algorithmic and Artificial Intelligence (AI) models. Boehm used the COCOMO model which is an algorithmic model in 1981 for SCE. The low accuracy and non-reliable structures of the algorithmic models led to high risks of software projects. So, it is needed to estimate the cost of the project annually and compare it to the other techniques. The Meta-Heuristic algorithms have been developed well lately in software fields and SCE. Meta-heuristic and Genetic Algorithms (GA) and Ant Colony Optimization (ACO) solve the problems according to the optimization of the problems and are very efficient in optimizing the algorithmic models and the effective factors in cost estimation. In this paper we have proposed a hybrid model based on GA and ACO for optimization of the effective factors' weight in NASA dataset software projects. The results of the experiments show that the proposed model is more efficient than COCOMO model in software projects cost estimation and holds less Magnitude of Relative Error (MRE) in comparison to COCOMO model.