The dedicated lanes management policy is a possible solution to the issues arising from the coexistence of human-driving vehicles (HDVs) and connected and autonomous vehicles (CAVs) in traffic. Although numerous studies have been conducted on the network deployment problem of CAV-dedicated lanes, the safety implications of CAV and its dedicated lanes are ignored. This study proposes a mathematical approach to optimize the deployment of CAV-dedicated lanes incorporating efficiency and safety concerns. An integrated framework is developed based on headway distributions to systematically evaluate the efficiency and safety performance of the road network. The platoon intensity index is utilized to model the platooning effect of CAVs on traffic safety and efficiency. A safety performance estimation method is proposed to account for the potential collision risk of mixed traffic flow and heterogeneity in car-following behavior. A bi-level programming model is adopted to solve the optimal deployment problem. The upper-level model is formulated as a bi-objective model to minimize the total travel time and the safety risk. The lower-level model describes the multi-class user equilibrium state of the CAV-HDV mixed traffic flow on the network. A genetic algorithm is utilized to solve the bi-level programming model and obtain the Pareto-optimal solution set. Two numerical studies are conducted to validate the proposed model and algorithm. The results revealed that the optimal deployment plan can significantly improve the road network’s safety and efficiency performance, whereas higher platoon intensities have a negative impact on traffic safety and efficiency for certain headway settings. Moreover, the results highlighted a trade-off between efficiency and safety in the optimal deployment problem, which may help decision-makers choose the optimal deployment plan based on the road network design needs.