Testing and Verification of Cyber-Physical Systems (CPS) is a challenging problem. The challenge arises as a result of the complex interactions between the components of these systems: the digital control, and the physical environment. Furthermore, the software complexity that governs the high-level control logic in these systems is increasing day by day. As a result, in recent years, both the academic community and the industry have been heavily invested in developing tools and methodologies for the development of safety-critical systems. One scalable approach in testing and verification of these systems is through guided system simulation using stochastic optimization techniques. The goal of the stochastic optimizer is to find system behavior that does not meet the intended specifications.

In this dissertation, three methods that facilitate the testing and verification process for CPS are presented:

1. A graphical formalism and tool which enables the elicitation of formal requirements. To evaluate the performance of the tool, a usability study is conducted.

2. A parameter mining method to infer, analyze, and visually represent falsifying ranges for parametrized system specifications.

3. A notion of conformance between a CPS model and implementation along with a testing framework.

The methods are evaluated over high-fidelity case studies from the industry.