How to configure fgoalattain correctly for a multiple functions multiple variables problem?

I am trying to design an operational amplifier (OpAmp) and prepared some functions that gives me the [Gain, Phase Margin (PM), Bandwidth (BW)] of an OpAmp given some devices as input. The devices affect the OpAmp parameters (Gain, PM, BW), and the devices are affected by changing 4 variables [Compensation Capacitors/Resistor (Cc/Rc), OpAmp Load Capacitance (C_Load), OpAmp Total Bias Current (Ib)].

Without going into details; I need to setup the following:

Make => [Gain > 71 dB, PM > 45 degrees, BW > 6.24e5], by minimizing (Ib), and choosing any bounded values for Cc/Rc and C_Load.

I am using fgoalattain to solve my problem, but I am not getting any solutions. However, trial and error shows me that if I set [Cc=150e-15, Rc=80e3, C_Load=10e-15, Ib=4.3e-6], I get [Gain = 71.2 dB, PM = 47 degrees, BW = 6.27e5]. This means that a solution exists, but I think I am not configuring my problem correctly. My code is shown below:

%% CMOS Fully-Differential Two-Stage Amplifier Analysis Optimization Module:
% -------------------------------------------------------------------------

% Clear workspace data:
clear;clc;close all;

% Load and prepare PDK devices data (gm/Id curves):
pdk_devices = get_pdk_devices();

%% -----------------------------------------
%  Prepare required devices init parameters:
%  -----------------------------------------

% Initialize devices structure:
% -----------------------------
devices = struct('M2',{0}, 'M4',{0}, 'M5',{0}, 'M6',{0},  'M8',{0}, 'M10',{0});

%% --------------------
%  Optimization Module:
%  --------------------
fun    = @(parameters)[get_2s_gain(pdk_devices, devices, parameters); get_2s_pm(pdk_devices, devices, parameters); get_2s_bw(pdk_devices, devices, parameters)];
goal   = [71, 45, 6.24e5];  % [Gain >= 71 dB, PM >= 45 degrees, BW >= 6.24e7]
weight = [1 1 1e3];
parameters0 = [20e-15  1e3    200e-15    3e-6]; % [CC Rc C_Load Ib]
lb          = [0       0      0          100e-9];
ub          = [20e-12  10e6   40e-12     30e-6];

options = optimoptions('fgoalattain');
options.MaxFunctionEvaluations = 60000;
options.MaxIterations = 60000;
options.ConstraintTolerance = 0*1e-15;
options.OptimalityTolerance = 1e-15;
options.StepTolerance = 0*1e-15;
options.FunctionTolerance = 1e-15;
options.FiniteDifferenceStepSize = 1e-15;
options.UseParallel = 1;
[parameters, fval, attainfc, exitflag] = fgoalattain(fun,parameters0,goal,weight,[],[],[],[],lb,ub,[],options);


fprintf('\nCc: %d \nRc:   %d \nC_Load:   %d\nIb:   %d',parameters(1), parameters(2), parameters(3), parameters(4));
fprintf('\n\nGain: %f \nPM:   %f \nBW:   %d\n',fval(1), fval(2), fval(3));
fprintf('\nAttain_factor:   %f\nExit Flag: %d\n',attainfc, exitflag);

My outputs are usually, no matter how large I change the limits:

Solver stopped prematurely.

fgoalattain stopped because it exceeded the function evaluation limit,
options.MaxFunctionEvaluations = 6.000000e+04.


Cc: 2.000000e-14 
Rc:   1000 
C_Load:   2.000000e-13
Ib:   3.000000e-06

Gain: 71.161912 
PM:   -7.906930 
BW:   1.556654e+05

Attain_factor:   0.161912
Exit Flag: 0

My questions are:

1) How do I program fgoalattain to know that I care about minimizing (Id) only and it can choose any values for (Rc, Cc, & C_Load)?

2) Is there a better way to address this optimization problem?