In both the clinic and the laboratory, efficacy estimators are used to estimate the shock strength required to achieve n given defibrillation success rate. In the clinic, efficacy estimators are used to estimate highly effective doses (i.e., the shock strength that defibrillates 95% of the time), in order to choose the setting for an ICD. Efficacy estimators are used in the laboratory to compare defibrillation techniques and configurations. Current efficacy estimators are inadequate because they are either difficult to use, can only estimate the shock strength that defibrillates 50% of the time, or do not yield desirable accuracy (low RMS error). This article presents a Bayesian estimation technique that forces the difference between successive test shock strengths (step-size) to be a fixed value after each measurement. Constraining the difference dramatically reduces the computational complexity of the up-down Bayesian method. This new, up-down Bayesian protocol can be used with up to 15 measurements to estimate the shock strength for any given success rate. Simulations show that the added constraint (fixed step-size) only slightly increases the rms error, as compared to the optimum Bayesian protocol. Our simulations also show that protocols can be generated for shock strengths rounded to the nearest 1, 10, or 50 V, without a great increase in RMS error. Experimental results from a subset of all the simulations are reported from six animals, showing a < - 2.4% difference between the simulated and measured errors.