Designing and implementing analog front-end circuits is a complex problem and thus, is the cornerstone of any radar system design. We propose removing the gain control block, as well as reducing the complexity by introducing a 1-bit analog to digital converter (ADC) at the receiving path. Nevertheless, this nonlinear quantization operation distorts the signal in a way that does not preserves its Gaussianity, rendering the common Maximum Likelihood (ML) based Direction of Arrival (DOA) estimation methods non-optimal. We derive the ML optimal DOA estimator for the 1-bit ADC and propose suboptimal, yet, effective estimator to reduce the complexity of the ML estimator. We benchmark the performance of the proposed estimators derived in this paper against the derived Cramér-Rao lower bound and investigate the case of a known and unknown transmitted signals. We show that the proposed algorithms attain the bound under various conditions as well as outperform a naïve ML approach for the 1-bit ADC problem.