You should use binomial logistic regression and not ordinal regression, though there are some concerns that you should be aware of when using ordinal predictors in a logistic regression.

Notably, if you're representing your ordinal variables numerically but the relationship between the rising levels of ordinality of an independent variable and the response of the dependent variable are not linear, the model can [falsely estimate the linear response][1].

For example, given an independent variable with ordinal levels `(a, b, c)` and a response variable, consider two possible variable responses:

**[A]**

[![enter image description here][2]][2]

**[B]**

[![enter image description here][3]][3]

In **[A]**, the ordinal predictors would very poorly model the binomial response, whereas in **B** the linear response would be just fine.

In **[A]**, you would definitely want to use the ordinal predictor as a categorical variable instead of as a numeric representation, whereas in **[B]** the numeric representation of ordinal levels would be just fine.

  [1]: http://pj.freefaculty.org/Papers/MidWest09/Midwest09.pdf
  [2]: https://i.sstatic.net/wyKEL.png
  [3]: https://i.sstatic.net/C1SlO.png