The shortage of warehouse labor has long been a thorn in the global supply chain, and it impacts the entire company as well as its customers. To meet unpredictable demand, companies have been heavily reliant on daily workers, but the high percentage of their use need to calculate again because of the difference skill with contractual worker. This research aim to create an optimization model using goal programming and particle swarm optimization (PSO) approach that can determine the required number of contract and daily workers and optimize the cost of labor, while ensuring that the number of daily workers does not exceed 10% of the total number of contract workers also to improve productivity and optimize resources in the warehouse. The results show that the model can significantly reduce the use of daily workers and optimize the distribution of manpower, resulting in a reduction of total manpower needed in the warehouse by around 25%. Moreover, the model optimization also leads to a cost savings of approximately 19% compared to the previous labor allocation system.