We address the problem of indirect quantization of a source subject to a mean-squared error distortion constraint. A well-known result of Wolf and Ziv is that the problem can be reduced to a standard (direct) quantization problem via a two-step approach: first apply the conditional expectation estimator, obtaining a ``new'' source, then solve for the optimal quantizer for the latter source. When quantization is implemented in hardware, however, invariably constraints on the allowable class of quantizers are imposed, typically limiting the class to \emph{time-invariant} scalar quantizers with contiguous quantization cells. In the present work, optimal indirect quantization subject to these constraints is considered. Necessary conditions an optimal quantizer within this class must satisfy are derived, in the form of generalized Lloyd-Max conditions, and an iterative algorithm for the design of such quantizers is proposed. Furthermore, for the case of a scalar observation, we derive a non-iterative algorithm for finding the optimal indirect quantizer based on dynamic programming.