Drive-by vehicle-borne mobile sensing with third-party vehicles has the advantages of high precision, low cost, appropriate coverage, and high timeliness, when compared to satellite-based, Unmanned Aerial Vehicle (UAV)-borne, or ground-based station monitoring. However, the non-prescriptive (or even unpredictable) behaviors of third-party vehicles can lead to imbalanced sampling. To obtain a good mobile sensing scheme with a maximum spatial coverage and a minimal spatial sampling heterogeneity, this study selected the best hybrid bus-taxi fleet to install sensors by proposing cooperative mobile sensing optimization models. As the traveling behavior patterns of taxis and buses mined from a huge amount of historical data were fully given consideration into optimization models, the sensors installed on the proposed mobile sensing taxi-bus fleet could automatically collect data, achieving the largest urban spatial range without operational intervention. Experimental results demonstrated the benefits of our solution in terms of global sensing ratios, sensing heterogeneity, cost savings, and geographical sampling equality. The proposed models can be used to monitor a variety of urban environmental objects, including air pollutants, noise, road roughness, and urban 3D scenes.