Considering the learning mechanism of new skills, a mathematical model of the software project scheduling problem is established. The model integrates some practical factors such as learning of new skills, increase of the new and existing skill proficiencies, and adaptive changes of dedications. Both duration and cost of the software project are minimized by finding the best assignment of employees to tasks. To solve the model, a discrete artificial bee colony algorithm incorporating heuristic information is proposed. A multi-learning strategy is applied to the employed bees phase to enhance the global search ability of the algorithm while maintaining the population diversity. In the onlooker bees phase, a mutation mechanism based on heuristic information is adopted, where information of the employees with higher fit in the optimal individual is retained, and distinct mutation operators are employed on different individuals based on their objective values to improve the local search ability. Experimental results show that compared with the existing methods, the proposed algorithm can find a better allocation in software project scheduling problems with increasing scales.