This is probably not a good thing to do. Looking at all the individual covariates first, and then building a model with those that are significant is logically equivalent to an automatic search procedure. While this approach is intuitive, inferences made from this procedure are not valid (e.g., the true p-values are different from those reported by software). The problem is magnified the larger the size of the initial set of covariates is. If you do this anyway (and, unfortunately, many people do), you cannot take the resulting model seriously. Instead, you must run an entirely new study, gathering an independent sample and fitting the previous model, to test it. However, this requires a lot of resources, and moreover, since the process is flawed and the previous model is likely a poor one, there is a strong chance it will not hold up--meaning that it is likely to waste a lot of resources.
A better way is to evaluate models of substantive interest to you. Then use an information criterion that penalizes model flexibility (such as the AIC) to adjudicate amongst those models. For logistic regression, the AIC is:
$AIC = -2*ln(likelihood) + 2*k$
where $k$ is the number of covariates included in that model. You want the model with the smallest value for the AIC, all things being equal. However, it is not always so simple; be wary when several models have similar values for the AIC, even though one may be lowest.
I include the complete formula for the AIC here, because different software outputs different information. You may have to calculate it from just the likelihood, or you may get the final AIC, or anything in between.