In the chemical industry, soft sensors are deployed to reduce equipment cost or allow for a continuous measurement of process variables. Soft sensors monitor parameters not via physical sensors but infer them from other process variables. On the one hand, the precision of a soft sensors is affected by its architecture, the choice of parametric equations like balances and thermodynamic or kinetic dependencies in the soft sensor model. On the other hand, uncertainty that is inherent to the input variable values propagates through the soft sensor model and impacts the output uncertainty. The latter is affected by the configuration of physical sensors in the chemical process. This paper proposes an approach for the combined optimization of soft sensor architecture and physical sensor configuration. For this purpose, the method combines an automatic extraction of all possible soft sensor architectures from a set of system equations with an uncertainty-based evaluation of sensor configurations. Applied to an in-situ Continuous Stirred Tank Reactor (CSTR), the method is capable of automatically identifying the optimal combination of soft sensor model and physical sensor configuration. It is further discussed, how the explainability of the results can be improved via variance-based uncertainty decomposition, by contributing soft sensor prediction uncertainty to individual inputs or operations along the calculation path.