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A key challenge for current and upcoming CMB lensing measurements is theirsensitivity to biases from extragalactic foregrounds, such as Sunyaev-Zeldovich(SZ) signals or cosmic infrared background emission. Several methods have beendeveloped to mitigate these lensing foreground biases, dividing broadly intomulti-frequency cleaning approaches and modifications to the estimatorgeometry, but how to optimally combine these methods has not yet been exploredin detail. In this paper, we examine which combination of lensing foregroundmitigation strategies is best able to reduce the impact of foregroundcontamination for a Simons-Observatory-like experiment while preserving maximalsignal-to-noise. Although the optimal combination obtained depends on whetherbias- or variance-reduction are prioritized and on whether polarization data isused, generally, we find that combinations involving both geometric (profilehardening, source hardening or shear) and multifrequency (symmetric cleaning)methods perform best. For lensing power spectrum measurements from temperature(polarization and temperature), our combined estimator methods are able toreduce the bias to the lensing amplitude below $\sigma/4$ or 0.3% (0.1%), afactor of 16 (30) lower than the standard QE bias, at a modest signal-to-noisecost of only 18% (12%). In contrast, single-method foreground-mitigationapproaches struggle to reduce the bias to a negligible level below $\sigma/2$without incurring a large noise penalty. For upcoming and current experiments,our combined methods therefore represent a promising approach for makinglensing measurements with negligible foreground bias.

Optimizing foreground mitigation for CMB lensing with combined multifrequency and geometric methods