This paper describes and implements three computationally attractive procedures for nonparametric estimation of mixing distributions in discrete choice models. The procedures are specific types of the well-known Expectation-Maximization (EM) algorithm based on three different ways of approximating the mixing distribution nonparametrically: (1) a discrete distribution with mass points and frequencies treated as parameters, (2) a discrete mixture of continuous distributions, with the moments and weight for each distribution treated as parameters, and (3) a discrete distribution with fixed mass points whose frequencies are treated as parameters. The methods are illustrated with a mixed logit model of households' choices among alternative-fueled vehicles.