Economic slowdown and construction demand shrinkage reduces the profit backlog for construction contractors and bites into their profit margin. The resulting fierce competition for jobs forces construction companies to look for more sophisticated analytical tools to analyze and improve their bidding strategies. For each contractor, bidding strategy is a decision-making process that is driven by the firm’s financial goals with the final objective of maximizing the firm’s gross profit and surpassing the breakeven point. This paper proposes a methodology to model and analyze different bidding strategies with hybrid agent based-system dynamics (ABSD) simulation. To capture the winning ratio of different bidding strategies, two generic types of agents are defined: “Regular” contractors, for whom the markup calculation process is only an internal decision; and “Learning” contractors, who, as adaptive agents, gradually enhance their prediction capabilities with learning mechanisms. The results of demonstrative simulations based on the proposed methodology show that the learning capability of a construction firm can effectively improve its bid-hit ratio and financial performance.