Structural health modeling is a critical technology to maintain the functional performance and reliability of civil infrastructures. To undertake such a task, data acquisition in field tests should be well conducted to ensure the adequate capture of dynamic response characteristics, based on which the underlying structural properties can be identified. Sensor placement is necessary to enable data collection and thus needs to be wisely guided with well-developed methodologies. This paper presents a generic sensor placement framework, in which four methodologies were developed and implemented by integrating with a genetic algorithm–based optimization tool to facilitate sensor placement optimization. This framework provides a number of features to enhance application flexibility and robustness from an engineering perspective. To validate the effectiveness of the developed framework, a comprehensive sensor placement case study was undertaken for the Factor Building at the University of California, Los Angeles (UCLA). The results obtained show good improvement of optimized sensor placement when compared with those instrumented by experience.