Software Project Scheduling Problem (SPSP) is one of the most critical issues in developing software. The major factor in completing the software project consistent with planned cost and schedule is implementing accurate and true scheduling. The subject of SPSP is an important topic which in software projects development and management should be considered over other topics and software project development must be done on it. SPSP usually includes resources planning, cost estimates, staffing and cost control. Therefore, it is necessary for SPSP use an algorithmic that with considering of costs and limited resources can estimate optimal time for completion of the project. Simultaneously reduce of time and cost in software projects development is very vital and necessary. The meta-heuristic algorithms has good performance in SPSP in recent years. When software projects factors are vague and incomplete, cost based scheduling models based on meta-heuristic algorithms can look better at scheduling. This algorithm works based on the Collective Intelligence and using the fitness function, it has more accurate ability for SPSP. In this paper, Differential Evolution (DE) algorithm is used to optimize SPSP. Experimental results show that the proposed model has better performance than Genetic Algorithm (GA).