This talk reports on 10 years of research that approached model-based verication and testing as a search and optimisation problem. The techniques presented rely on system models describing the task architecture and performance characteristics. As a strategy to ease adoption in practice, those models rely on standards (e.g., UML/MARTE) or lightweight extensions enabling the use of commercial or open source modeling platforms to support automation. Once the required information is extracted from models, early veri cation and testing both consist in identifying scenarios that maximize chances of uncovering concurrency and performance issues, such as deadline misses, starvation, or unacceptable levels of CPU usage. To do so, we either rely on evolutionary computation or constraint optimization, for which e ective support already exists. The main challenge is of course to transform each speci c problem into a search or constraint optimization problem, in such a way that these technologies can be e cient and scale.