The transition to new energy in the public transportation sector has become a critical strategy for achieving sustainable urban development and reducing carbon emissions. This study analyses the impact of bus fleet energy composition, specifically the proportions of battery electric buses (BEBs), liquefied natural gas (LNG) buses, and diesel buses, on operator costs and carbon emissions in urban transit networks. A multi-objective optimization model is proposed to minimize passenger travel costs, operator costs, and carbon emissions, while incorporating constraints on the minimum proportions of buses with different energy types to reflect the trend of transitioning to new energy in the public transportation industry. And the NSGA-II algorithm is employed to solve the model. A case study of Beijing’s central urban area is conducted to validate the model, which demonstrates that increasing the proportion of BEBs with conventional electricity from 75% to 94% reduces carbon emissions by 0.68%, while adopting green electricity for BEBs achieves a more substantial 56% reduction. Sensitivity analysis highlights the trade-offs between emission reductions and operator costs, revealing that carbon trading revenues alone are insufficient to offset the higher costs of BEBs adoption.